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Li F, Li D, Wang C, Liu G, Wang R, Ren H, Tang Y, Wang Y, Chen Y, Liang K, Huang Q, Sawan M, Qiu M, Wang H, Zhu B. An artificial visual neuron with multiplexed rate and time-to-first-spike coding. Nat Commun 2024; 15:3689. [PMID: 38693165 PMCID: PMC11063071 DOI: 10.1038/s41467-024-48103-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.
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Affiliation(s)
- Fanfan Li
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Dingwei Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Chuanqing Wang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
| | - Guolei Liu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Rui Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China
| | - Huihui Ren
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yingjie Tang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yan Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yitong Chen
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Kun Liang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Qi Huang
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Min Qiu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China.
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China.
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China.
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China.
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Zhao S, Wang C, Fang C, Tian F, Yang J, Sawan M. HybMED: A Hybrid Neural Network Training Processor with Multi-Sparsity Exploitation for Internet of Medical Things. IEEE Trans Biomed Circuits Syst 2024; PP:1-12. [PMID: 38630572 DOI: 10.1109/tbcas.2024.3389875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical Things (AIoMT) applications suffer from accuracy degradation due to physiological signal variations among patients. On-chip learning can overcome this issue by online adaptation of neural network parameters for user-specific tasks. However, existing on-chip learning processors have limitations in terms of versatility, resource utilization, and energy efficiency. We propose HybMED, which is a novel neural signal processor that supports on-chip hybrid neural network training using a composite direct feedback alignment-based paradigm. HybMED is suitable for general-purpose health monitoring AIoMT devices. It improves resource utilization and area efficiency by the reconfigurable homogeneous core with heterogeneous data flow and enhances energy efficiency by exploiting sparsity at different granularities. The chip was fabricated by TSMC 40nm process and tested in multiple physiological signal processing tasks, demonstrating an average improvement in accuracy of 41.16% following online few-shot learning. The chip demonstrates an area efficiency of 1.17 GOPS/mm2 and an energy efficiency of 1.58 TOPS/W. Compared to the previous state-of-the-art physiological signal processors with on-chip learning, the chip achieves a 65× improvement in area efficiency and 1.48× improvement in energy efficiency, respectively.
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Eskandari R, Sawan M. Challenges and Perspectives on Impulse Radio-Ultra-Wideband Transceivers for Neural Recording Applications. IEEE Trans Biomed Circuits Syst 2024; 18:369-382. [PMID: 37938944 DOI: 10.1109/tbcas.2023.3331049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Brain-machine interfaces (BMI) are widely adopted in neuroscience investigations and neural prosthetics, with sensing channel counts constantly increasing. These Investigations place increasing demands for high data rates and low-power implantable devices despite high tissue losses. The Impulse radio ultra-wideband (IR-UWB), a revived wireless technology for short-range radios, has been widely used in various applications. Since the requirements and solutions are application-oriented, in this review paper we focus on neural recording implants with high-data rates and ultra-low power requirements. We examine in detail the working principle, design methodology, performance, and implementations of different architectures of IR-UWB transceivers in a quantitative manner to draw a deep comparison and extract the bottlenecks and possible solutions concerning the dedicated application. Our analysis shows that current solutions rely on enhanced or combined modulation techniques to improve link margin. An in-depth study of prior-art publications that achieved Gbps data rates concludes that edge-combination architecture and non-coherent detectors are remarkable for transmitter and receiver, respectively. Although the aim to minimize power and improve data rate - defined as energy efficiency (pJ/b) - extending communication distance despite high tissue losses and limited power budget, good narrow-band interference (NBI) tolerance coexisted in the same frequency band of UWB systems, and compatibility with energy harvesting designs are among the critical challenges remained unsolved. Furthermore, we expect that the combination of artificial intelligence (AI) and the inherent advantages of UWB radios will pave the way for future improvements in BMIs.
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Yang X, Chai C, Zuo H, Chen YH, Shi J, Ma C, Sawan M. Monte Carlo-Based Optical Simulation of Optical Distribution in Deep Brain Tissues Using Sixteen Optical Sources. Bioengineering (Basel) 2024; 11:260. [PMID: 38534534 DOI: 10.3390/bioengineering11030260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 03/28/2024] Open
Abstract
Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. To overcome the difficulty in successful brain imaging applications, we conducted a simulation using 16 optical source types within a brain model that is based on the Monte Carlo method. In addition, we propose an evaluation method of the optical propagating depth and resolution, specifically one based on the optical distribution for brain applications. Based on the results, the best optical source types were determined in each layer. The maximum propagating depth and corresponding source were extracted. The optical source propagating field width was acquired in different depths. The maximum and minimum widths, as well as the corresponding source, were determined. This paper provides a reference for evaluating the optical propagating depth and resolution from an optical simulation aspect, and it has the potential to optimize the performance of optical-based techniques.
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Affiliation(s)
- Xi Yang
- College of Biomedical Engineering & Instrument Science, Zhejiang University, 38 Zheda Road, Hangzhou 310013, China
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Street, Xihu District, Hangzhou 310024, China
| | - Chengpeng Chai
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Street, Xihu District, Hangzhou 310024, China
| | - Hongzhi Zuo
- Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, 30, Shuangqing Road, Haidian District, Beijing 100084, China
| | - Yun-Hsuan Chen
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Street, Xihu District, Hangzhou 310024, China
| | - Junhui Shi
- Zhejiang Lab, 1 Kechuang Avenue, Yuhang District, Hangzhou 311100, China
| | - Cheng Ma
- Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, 30, Shuangqing Road, Haidian District, Beijing 100084, China
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Street, Xihu District, Hangzhou 310024, China
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Ma Z, Chen C, Lin Y, Qi L, Li Y, Bi X, Sawan M, Wang G, Zhao J. An Energy-Efficient FD-fNIRS Readout Circuit Employing a Mixer-First Analog Frontend and a Σ-Δ Phase-to-Digital Converter. IEEE Trans Biomed Circuits Syst 2024; PP:1-12. [PMID: 38437071 DOI: 10.1109/tbcas.2024.3372887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
This paper presents a low-power frequency-domain functional near-infrared spectroscopy (FD-fNIRS) readout circuit for the absolute value measurement of tissue optical characteristics. The paper proposes a mixer-first analog front-end (AFE) structure and a 1-bit Σ-Δ phase-to-digital converter (PDC) to reduce the required circuit bandwidth and the laser modulation frequency, thereby saving power while maintaining high resolution. The proposed chip achieves sub-0.01° phase resolution and consumes 6.8 mW of power. Nine optical solid phantoms are produced to evaluate the chip. Compared to a self-built high-precision measurement platform that combines a network analyzer with an avalanche photodiode (APD) module, the maximum measuring errors of the absorption coefficient and reduced scattering coefficient are 10.6% and 12.3%, respectively.
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Wu H, Cai C, Ming W, Chen W, Zhu Z, Feng C, Jiang H, Zheng Z, Sawan M, Wang T, Zhu J. Speech decoding using cortical and subcortical electrophysiological signals. Front Neurosci 2024; 18:1345308. [PMID: 38486966 PMCID: PMC10937352 DOI: 10.3389/fnins.2024.1345308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024] Open
Abstract
Introduction Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures' potential contributions to speech decoding in brain-computer interfaces. Methods In this study, stereotactic electroencephalography (sEEG) was employed to investigate subcortical structures' role in speech decoding. Two native Mandarin Chinese speakers, undergoing sEEG implantation for epilepsy treatment, participated. Participants read Chinese text, with 1-30, 30-70, and 70-150 Hz frequency band powers of sEEG signals extracted as key features. A deep learning model based on long short-term memory assessed the contribution of different brain structures to speech decoding, predicting consonant articulatory place, manner, and tone within single syllable. Results Cortical signals excelled in articulatory place prediction (86.5% accuracy), while cortical and subcortical signals performed similarly for articulatory manner (51.5% vs. 51.7% accuracy). Subcortical signals provided superior tone prediction (58.3% accuracy). The superior temporal gyrus was consistently relevant in speech decoding for consonants and tone. Combining cortical and subcortical inputs yielded the highest prediction accuracy, especially for tone. Discussion This study underscores the essential roles of both cortical and subcortical structures in different aspects of speech decoding.
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Affiliation(s)
- Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Clinical Research Center for Neurological Disease of Zhejiang Province, Hangzhou, China
| | - Chengwei Cai
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenjie Ming
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wangyu Chen
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhoule Zhu
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chen Feng
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongjie Jiang
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhe Zheng
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mohamad Sawan
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou, China
| | - Ting Wang
- School of Foreign Languages, Tongji University, Shanghai, China
- Center for Speech and Language Processing, Tongji University, Shanghai, China
| | - Junming Zhu
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Wang P, Fredj Z, Zhang H, Rong G, Bian S, Sawan M. Blocking Superantigen-Mediated Diseases: Challenges and Future Trends. J Immunol Res 2024; 2024:2313062. [PMID: 38268531 PMCID: PMC10807946 DOI: 10.1155/2024/2313062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/15/2023] [Accepted: 12/30/2023] [Indexed: 01/26/2024] Open
Abstract
Superantigens are virulence factors secreted by microorganisms that can cause various immune diseases, such as overactivating the immune system, resulting in cytokine storms, rheumatoid arthritis, and multiple sclerosis. Some studies have demonstrated that superantigens do not require intracellular processing and instated bind as intact proteins to the antigen-binding groove of major histocompatibility complex II on antigen-presenting cells, resulting in the activation of T cells with different T-cell receptor Vβ and subsequent overstimulation. To combat superantigen-mediated diseases, researchers have employed different approaches, such as antibodies and simulated peptides. However, due to the complex nature of superantigens, these approaches have not been entirely successful in achieving optimal therapeutic outcomes. CD28 interacts with members of the B7 molecule family to activate T cells. Its mimicking peptide has been suggested as a potential candidate to block superantigens, but it can lead to reduced T-cell activity while increasing the host's infection risk. Thus, this review focuses on the use of drug delivery methods to accurately target and block superantigens, while reducing the adverse effects associated with CD28 mimic peptides. We believe that this method has the potential to provide an effective and safe therapeutic strategy for superantigen-mediated diseases.
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Affiliation(s)
- Pengbo Wang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China
| | - Zina Fredj
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China
| | - Hongyong Zhang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China
| | - Guoguang Rong
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China
| | - Sumin Bian
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China
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Rong G, Sawan M. Tamm Plasmon Polariton Biosensors Based on Porous Silicon: Design, Validation and Analysis. Biosensors (Basel) 2023; 13:1026. [PMID: 38131786 PMCID: PMC10742303 DOI: 10.3390/bios13121026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023]
Abstract
Tamm Plasmon Polariton (TPP) is a nanophotonic phenomenon that has attracted much attention due to its spatial strong field confinement, ease of mode excitation, and polarization independence. TPP has applications in sensing, storage, lasing, perfect absorber, solar cell, nonlinear optics, and many others. In this work, we demonstrate a biosensing platform based on TPP resonant mode. Both theoretical analyses based on the transfer matrix method and experimental validation through nonspecific detection of liquids of different refractive indices and specific detection of SARS-CoV-2 nucleocapsid protein (N-protein) are presented. Results show that the TPP biosensor has high sensitivity and good specificity. For N-protein detection, the sensitivity can be up to 1.5 nm/(µg/mL), and the limit of detection can reach down to 7 ng/mL with a spectrometer of 0.01 nm resolution in wavelength shift. Both nonspecific detection of R.I. liquids and specific detection of N-protein have been simulated and compared with experimental results to demonstrate consistency. This work paves the way for design, optimization, fabrication, characterization, and performance analysis of TPP based biosensors.
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Affiliation(s)
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China;
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Yan S, Sun J, Chen B, Wang L, Bian S, Sawan M, Tang H, Wen L, Meng G. Manipulating Coupled Field Enhancement in Slot-under-Groove Nanoarrays for Universal Surface-Enhanced Raman Scattering. ACS Nano 2023; 17:22766-22777. [PMID: 37782470 DOI: 10.1021/acsnano.3c07458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Surface-enhanced Raman scattering (SERS) is an ultrasensitive spectroscopic technique that can identify materials and chemicals based on their inelastic light-scattering properties. In general, SERS relies on sub-10 nm nanogaps to amplify the Raman signals and achieve ultralow-concentration identification of analytes. However, large-sized analytes, such as proteins and viruses, usually cannot enter these tiny nanogaps, limiting the practical applications of SERS. Herein, we demonstrate a universal SERS platform for the reliable and sensitive identification of a wide range of analytes. The key to this success is the prepared "slot-under-groove" nanoarchitecture arrays, which could realize a strongly coupled field enhancement with a large spatial mode distribution via the hybridization of gap-surface plasmons in the upper V-groove and localized surface plasmon resonance in the lower slot. Therefore, our slot-under-groove platform can simultaneously deliver high sensitivity for small-sized analytes and the identification of large-sized analytes with a large Raman gain.
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Affiliation(s)
- Sisi Yan
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, P.O. Box 1129, Hefei 230031, China
- Department of Materials Science and Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China
| | - Jiacheng Sun
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
| | - Bin Chen
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, P.O. Box 1129, Hefei 230031, China
- Department of Materials Science and Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China
| | - Lang Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
| | - Sumin Bian
- CenBRAIN Lab, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
| | - Mohamad Sawan
- CenBRAIN Lab, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
| | - Haibin Tang
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, P.O. Box 1129, Hefei 230031, China
- Department of Materials Science and Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China
| | - Liaoyong Wen
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
| | - Guowen Meng
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, P.O. Box 1129, Hefei 230031, China
- Department of Materials Science and Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China
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Xia F, Li H, Li Y, Liu X, Xu Y, Fang C, Hou Q, Lin S, Zhang Z, Yang J, Sawan M. Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea. Sensors (Basel) 2023; 23:8882. [PMID: 37960581 PMCID: PMC10648123 DOI: 10.3390/s23218882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
A hypoglossal nerve stimulator (HGNS) is an invasive device that is used to treat obstructive sleep apnea (OSA) through electrical stimulation. The conventional implantable HGNS device consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma. In this paper, we propose a minimally invasive HGNS based on an electrocardiogram (ECG) sensor and wireless power transfer (WPT), consisting of a wearable breathing monitor and an implantable stimulator. The breathing external monitor utilizes an ECG sensor to identify abnormal breathing patterns associated with OSA with 88.68% accuracy, achieved through the utilization of a convolutional neural network (CNN) algorithm. With a skin thickness of 5 mm and a receiving coil diameter of 9 mm, the power conversion efficiency was measured as 31.8%. The implantable device, on the other hand, is composed of a front-end CMOS power management module (PMM), a binary-phase-shift-keying (BPSK)-based data demodulator, and a bipolar biphasic current stimuli generator. The PMM, with a silicon area of 0.06 mm2 (excluding PADs), demonstrated a power conversion efficiency of 77.5% when operating at a receiving frequency of 2 MHz. Furthermore, it offers three-voltage options (1.2 V, 1.8 V, and 3.1 V). Within the data receiver component, a low-power BPSK demodulator was ingeniously incorporated, consuming only 42 μW when supplied with a voltage of 0.7 V. The performance was achieved through the implementation of the self-biased phase-locked-loop (PLL) technique. The stimuli generator delivers biphasic constant currents, providing a 5 bit programmable range spanning from 0 to 2.4 mA. The functionality of the proposed ECG- and WPT-based HGNS was validated, representing a highly promising solution for the effective management of OSA, all while minimizing the trauma and space requirements.
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Affiliation(s)
- Fen Xia
- Zhejiang University, Hangzhou 310024, China;
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Hanrui Li
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
- SAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Yixi Li
- State Key Laboratory of Superlattices, Microstructures Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100045, China;
| | - Xing Liu
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Yankun Xu
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Chaoming Fang
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Qiming Hou
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Siyu Lin
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Zhao Zhang
- SAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Jie Yang
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Mohamad Sawan
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
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Song X, Fredj Z, Zheng Y, Zhang H, Rong G, Bian S, Sawan M. Biosensors for waterborne virus detection: Challenges and strategies. J Pharm Anal 2023; 13:1252-1268. [PMID: 38174120 PMCID: PMC10759259 DOI: 10.1016/j.jpha.2023.08.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/20/2023] [Accepted: 08/29/2023] [Indexed: 01/05/2024] Open
Abstract
Waterborne viruses that can be harmful to human health pose significant challenges globally, affecting health care systems and the economy. Identifying these waterborne pathogens is essential for preventing diseases and protecting public health. However, handling complex samples such as human and wastewater can be challenging due to their dynamic and complex composition and the ultralow concentration of target analytes. This review presents a comprehensive overview of the latest breakthroughs in waterborne virus biosensors. It begins by highlighting several promising strategies that enhance the sensing performance of optical and electrochemical biosensors in human samples. These strategies include optimizing bioreceptor selection, transduction elements, signal amplification, and integrated sensing systems. Furthermore, the insights gained from biosensing waterborne viruses in human samples are applied to improve biosensing in wastewater, with a particular focus on sampling and sample pretreatment due to the dispersion characteristics of waterborne viruses in wastewater. This review suggests that implementing a comprehensive system that integrates the entire waterborne virus detection process with high-accuracy analysis could enhance virus monitoring. These findings provide valuable insights for improving the effectiveness of waterborne virus detection, which could have significant implications for public health and environmental management.
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Affiliation(s)
- Xixi Song
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Zina Fredj
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Yuqiao Zheng
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Hongyong Zhang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Guoguang Rong
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Sumin Bian
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
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Zhang H, Wang P, Huang N, Zhao L, Su Y, Li L, Bian S, Sawan M. Single neurons on microelectrode array chip: manipulation and analyses. Front Bioeng Biotechnol 2023; 11:1258626. [PMID: 37829565 PMCID: PMC10565505 DOI: 10.3389/fbioe.2023.1258626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
Chips-based platforms intended for single-cell manipulation are considered powerful tools to analyze intercellular interactions and cellular functions. Although the conventional cell co-culture models could investigate cell communication to some extent, the role of a single cell requires further analysis. In this study, a precise intercellular interaction model was built using a microelectrode array [microelectrode array (MEA)]-based and dielectrophoresis-driven single-cell manipulation chip. The integrated platform enabled precise manipulation of single cells, which were either trapped on or transferred between electrodes. Each electrode was controlled independently to record the corresponding cellular electrophysiology. Multiple parameters were explored to investigate their effects on cell manipulation including the diameter and depth of microwells, the geometry of cells, and the voltage amplitude of the control signal. Under the optimized microenvironment, the chip was further evaluated using 293T and neural cells to investigate the influence of electric field on cells. An examination of the inappropriate use of electric fields on cells revealed the occurrence of oncosis. In the end of the study, electrophysiology of single neurons and network of neurons, both differentiated from human induced pluripotent stem cells (iPSC), was recorded and compared to demonstrate the functionality of the chip. The obtained preliminary results extended the nature growing model to the controllable level, satisfying the expectation of introducing more elaborated intercellular interaction models.
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Affiliation(s)
- Hongyong Zhang
- Zhejiang University, Hangzhou, Zhejiang, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Pengbo Wang
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Nan Huang
- School of Life Science, Westlake University, Hangzhou, China
| | - Lingrui Zhao
- School of Life Science, Westlake University, Hangzhou, China
| | - Yi Su
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Lingfei Li
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sumin Bian
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Mohamad Sawan
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
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Rong G, Xu Y, Sawan M. Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors. Biosensors (Basel) 2023; 13:860. [PMID: 37754094 PMCID: PMC10526989 DOI: 10.3390/bios13090860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/28/2023]
Abstract
We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.
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Affiliation(s)
| | | | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China; (G.R.); (Y.X.)
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Su Y, Bian S, Pan D, Xu Y, Rong G, Zhang H, Sawan M. Heterogeneous-Nucleation Biosensor for Long-Term Collection and Mask-Based Self-Detection of SARS-CoV-2. Biosensors (Basel) 2023; 13:858. [PMID: 37754092 PMCID: PMC10526364 DOI: 10.3390/bios13090858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 08/22/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023]
Abstract
The effective control of infectious diseases, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, depends on the availability of rapid and accurate monitoring techniques. However, conventional SARS-CoV-2 detection technologies do not support continuous self-detection and may lead to cross-infection when utilized in medical institutions. In this study, we introduce a prototype of a mask biosensor designed for the long-term collection and self-detection of SARS-CoV-2. The biosensor utilizes the average resonance Rayleigh scattering intensity of Au nanocluster-aptamers. The inter-mask surface serves as a medium for the long-term collection and concentration enhancement of SARS-CoV-2, while the heterogeneous-nucleation nanoclusters (NCs) contribute to the exceptional stability of Au NCs for up to 48 h, facilitated by the adhesion of Ti NCs. Additionally, the biosensors based on Au NC-aptamers exhibited high sensitivity for up to 1 h. Moreover, through the implementation of a support vector machine classifier, a significant number of point signals can be collected and differentiated, leading to improved biosensor accuracy. These biosensors offer a complementary wearable device-based method for diagnosing SARS-CoV-2, with a limit of detection of 103 copies. Given their flexibility, the proposed biosensors possess tremendous potential for the continuous collection and sensitive self-detection of SARS-CoV-2 variants and other infectious pathogens.
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Affiliation(s)
- Yi Su
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310013, China; (Y.S.); (D.P.)
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China; (S.B.); (Y.X.); (G.R.); (H.Z.)
| | - Sumin Bian
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China; (S.B.); (Y.X.); (G.R.); (H.Z.)
| | - Dingyi Pan
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310013, China; (Y.S.); (D.P.)
| | - Yankun Xu
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China; (S.B.); (Y.X.); (G.R.); (H.Z.)
| | - Guoguang Rong
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China; (S.B.); (Y.X.); (G.R.); (H.Z.)
| | - Hongyong Zhang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China; (S.B.); (Y.X.); (G.R.); (H.Z.)
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou 310030, China; (S.B.); (Y.X.); (G.R.); (H.Z.)
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15
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Fredj Z, Wang P, Ullah F, Sawan M. A nanoplatform-based aptasensor to electrochemically detect epinephrine produced by living cells. Mikrochim Acta 2023; 190:343. [PMID: 37540351 DOI: 10.1007/s00604-023-05902-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/04/2023] [Indexed: 08/05/2023]
Abstract
A novel aptasensor has been designed for quantitative monitoring of epinephrine (EP) based on cerium metal-organic framework (CeMOF) loaded gold nanoparticles (AuNPs). The aptamer, specific to EP, is immobilized on a flexible screen-printed electrode modified with AuNPs@CeMOF, enabling highly selective binding to the target biomolecule. Under optimized operational conditions, the peak currents using voltammetric detection measured at voltage of 83 mV (vs. Ag/AgCl) for epinephrine exhibit a linear increase within concentration in the range 1 pM-10 nM. Following this detection strategy, a boasted limit of detection of 0.3 pM was achieved, surpassing the sensitivity of most reported methods. The developed biosensor showcased exceptional performance in detection of EP in spiked serum sample, with remarkable recovery range of 95.8-113% and precision reflected by low relative standard deviation (RSD) ranging from 2.23 to 6.19%. These results indicate the potential utility of this biosensor as a valuable clinical diagnostic tool. Furthermore, in vitro experiments were carried out using the presented biosensor to monitor the release of epinephrine from PC12 cells upon extracellular stimulation with K+ ions.
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Affiliation(s)
- Zina Fredj
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Pengbo Wang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Fateh Ullah
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, 310030, China.
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16
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Wu H, Chen J, Liu X, Zou W, Yang J, Sawan M. An Energy-Efficient Small-Area Configurable Analog Front-End Interface for Diverse Biosignals Recording. IEEE Trans Biomed Circuits Syst 2023; 17:818-830. [PMID: 37428667 DOI: 10.1109/tbcas.2023.3293492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
We introduce a fully integrated configurable analog front-end (CAFE) sensor intended to accommodate various types of bio-potential signals in this article. The proposed CAFE is composed of an AC-coupled chopper-stabilized amplifier to effectively reduce 1/f noise and an energy- and area-efficient tunable filter to tune this interface to the bandwidth of various specific signals of interest. A tunable active-pseudo-resistor is integrated into the amplifier's feedback to realize a reconfigurable high-pass cutoff frequency and enhance its linearity, while the filter is designed using a subthreshold-source-follower-based pseudo-RC (SSF-PRC) topology to attain the required super-low cutoff frequency without the need for extremely low biasing current sources. Implemented in TSMC 40 nm technology, the chip occupies an active area of 0.048 [Formula: see text] while consuming 2.47 μW DC power from a 1.2-V supply voltage. Measurement results indicate that the proposed design achieved a mid-band gain of 37 dB, with an integrated input-referred noise ( VIRN) of 1.7 μVrms within 1-260 Hz. The total harmonic distortion (THD) of the CAFE is below 1 % with a 2.4 m Vpp input signal. With a wide-range bandwidth adjustment capability, the proposed CAFE can be used in both wearable and implantable recording devices to acquire different bio-potential signals.
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Wu D, Yang J, Sawan M. Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond. IEEE Trans Neural Syst Rehabil Eng 2023; PP:1-1. [PMID: 37450364 DOI: 10.1109/tnsre.2023.3295453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Machine learning on electromyography (EMG) has recently achieved remarkable success on various tasks, while such success relies heavily on the assumption that the training and future data must be of the same data distribution. However, this assumption may not hold in many real-world applications. Model calibration is required via data re-collection and label annotation, which is generally very expensive and time-consuming. To address this issue, transfer learning (TL), which aims to improve target learners' performance by transferring knowledge from related source domains, is emerging as a new paradigm to reduce the amount of calibration effort. This survey assesses the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications. Unlike previous surveys on purely transfer learning or EMG-based machine learning, this survey aims to provide insight into the biological foundations of existing transfer learning methods on EMG-related analysis. Specifically, we first introduce the muscles' physiological structure, the EMG generating mechanism, and the recording of EMG to provide biological insights behind existing transfer learning approaches. Further, we categorize existing research endeavors into data based, model based, training scheme based, and adversarial based. This survey systematically summarizes and categorizes existing transfer learning approaches for EMG related machine learning applications. In addition, we discuss possible drawbacks of existing works and point out the future direction of better EMG transfer learning algorithms to enhance practicality for real-world applications.
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Gagliano L, Chang A, Shokooh LA, Toffa DH, Lesage F, Sawan M, Nguyen DK, Assi EB. Cross-bispectrum connectivity of intracranial EEG: A novel approach to seizure onset zone localization. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38082787 DOI: 10.1109/embc40787.2023.10340885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and β (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.
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Fang C, Wang C, Zhao S, Tian F, Yang J, Sawan M. A 510μW 0.738-mm2 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor with Unified Computation Engine in 40-nm CMOS. IEEE Trans Biomed Circuits Syst 2023; PP:1-14. [PMID: 37224372 DOI: 10.1109/tbcas.2023.3279367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Implementing neural networks (NN) on edge devices enables AI to be applied in many daily scenarios. The stringent area and power budget on edge devices impose challenges on conventional NNs with massive energy-consuming Multiply Accu- mulation (MAC) operations and offer an opportunity for Spiking Neural Networks (SNN), which can be implemented within sub- mW power budget. However, mainstream SNN topologies varies from Spiking Feedforward Neural Network (SFNN), Spiking Recurrent Neural Network (SRNN), to Spiking Convolutional Neural Network (SCNN), and it is challenging for the edge SNN processor to adapt to different topologies. Besides, online learning ability is critical for edge devices to adapt to local environments but comes with dedicated learning modules, further increasing area and power consumption burdens. To alleviate these prob- lems, this work proposed RAINE, a reconfigurable neuromorphic engine supporting multiple SNN topologies and a dedicated trace- based rewarded spike-timing-dependent plasticity (TR-STDP) learning algorithm. Sixteen Unified-Dynamics Learning-Engines (UDLEs) are implemented in RAINE to realize a compact and reconfigurable implementation of different SNN operations. Three topology-aware data reuse strategies are proposed and analyzed to optimize the mapping of different SNNs on RAINE. A 40-nm prototype chip is fabricated, achieving energy-per- synaptic-operation (SOP) of 6.2 pJ/SOP at 0.51V, and power consumption of 510 μW at 0.45V. Finally, three examples with dif- ferent SNN topologies, including SRNN-based ECG arrhythmia detection, SCNN-based 2D image classification, and end-to-end on-chip learning for MNIST digit recognition, are demonstrated on RAINE with ultra-low energy consumption of 97.7nJ/step, 6.28μJ/sample, and 42.98μJ/sample respectively. These results show the feasibility of obtaining high reconfigurability and low power consumption simultaneously on a SNN processor.
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Wang C, Fang C, Zou Y, Yang J, Sawan M. SpikeSEE: An energy-efficient dynamic scenes processing framework for retinal prostheses. Neural Netw 2023; 164:357-368. [PMID: 37167749 DOI: 10.1016/j.neunet.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 04/29/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023]
Abstract
Intelligent and low-power retinal prostheses are highly demanded in this era, where wearable and implantable devices are used for numerous healthcare applications. In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses. The spike representation encoding technique could interpret dynamic scenes with sparse spike trains, decreasing the data volume. The SRNN model, inspired by the human retina's special structure and spike processing method, is adopted to predict the response of ganglion cells to dynamic scenes. Experimental results show that the Pearson correlation coefficient of the proposed SRNN model achieves 0.93, which outperforms the state-of-the-art processing framework for retinal prostheses. Thanks to the spike representation and SRNN processing, the model can extract visual features in a multiplication-free fashion. The framework achieves 8 times power reduction compared with the convolutional recurrent neural network (CRNN) processing-based framework. Our proposed SpikeSEE predicts the response of ganglion cells more accurately with lower energy consumption, which alleviates the precision and power issues of retinal prostheses and provides a potential solution for wearable or implantable prostheses.
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Affiliation(s)
- Chuanqing Wang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China
| | - Chaoming Fang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China
| | - Yong Zou
- Beijing Institute of Radiation Medicine, Beijing, 100850, China
| | - Jie Yang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China.
| | - Mohamad Sawan
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China.
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Zhao S, Yang J, Wang J, Fang C, Liu T, Zhang S, Sawan M. A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses. IEEE Trans Biomed Circuits Syst 2023; PP. [PMID: 37074883 DOI: 10.1109/tbcas.2023.3268502] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency. 1) Hybrid neural network design: The processor supports artificial neural network (ANN)- and spiking neural network (SNN)-based neuromorphic processing where ANN is used to support the processing of ExG signals and SNN is used for handling neural spike signals. 2) Event-driven processing: The processor can perform always-on binary neural network (BNN)-based event detection with low-energy consumption, and it only switches to the high-accuracy convolutional neural network (CNN)-based recognition mode when events are detected. 3) Reconfigurable architecture: By exploiting the computational similarity of different neural networks, the processor supports critical BNN, CNN, and SNN operations with the same processing elements, achieving significant area reduction and energy efficiency improvement over those of a naive implementation. It achieves 90.05% accuracy and 4.38 uJ/class in a center-out reaching task with an SNN and 99.4% sensitivity, 98.6% specificity, and 1.93 uJ/class in an EEG-based seizure prediction task with dual neural network-based event-driven processing. Moreover, it achieves a classification accuracy of 99.92%, 99.38%, and 86.39% and energy consumption of 1.73, 0.99, and 1.31 uJ/class for EEG-based epileptic seizure detection, ECG-based arrhythmia detection, and EMG-based gesture recognition, respectively.
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Wu D, Zhao S, Yang J, Sawan M. Software-Hardware Co-Design for Energy-Efficient Continuous Health Monitoring via Task-Aware Compression. IEEE Trans Biomed Circuits Syst 2023; 17:180-191. [PMID: 37022054 DOI: 10.1109/tbcas.2023.3238719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Low power consumption associated with data transmission and processing of wearable/implantable devices is crucial to ensure the usability of continuous health monitoring systems. In this paper, we propose a novel health monitoring framework where the signal acquired is compressed in a task-aware manner to preserve task-relevant information at the sensor end with a low computation cost. The resulting compressed signals can be transmitted with significantly lower bandwidth, analyzed directly without a dedicated reconstruction process, or reconstructed with high fidelity. Also, we propose a dedicated hardware architecture with sparse Booth encoding multiplication and the 1-D convolution pipeline for the task-aware compression and the analysis modules, respectively. Extensive experiments show that the proposed framework is accurate, with a seizure prediction accuracy of 89.70 % under a signal compression ratio of 1/16. The hardware architecture is implemented on an Alveo U250 FPGA board, achieving a power of 0.207 W at a clock frequency of 100 MHz.
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Zheng Y, Song X, Fredj Z, Bian S, Sawan M. Challenges and perspectives of multi-virus biosensing techniques: A review. Anal Chim Acta 2023; 1244:340860. [PMID: 36737150 PMCID: PMC9868144 DOI: 10.1016/j.aca.2023.340860] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023]
Abstract
In the context of globalization, individuals have an increased chance of being infected by multiple viruses simultaneously, thereby highlighting the importance of developing multiplexed devices. In addition to sufficient sensitivity and rapid response, multi-virus sensing techniques are expected to offer additional advantages including high throughput, one-time sampling for parallel analysis, and full automation with data visualization. In this paper, we review the optical, electrochemical, and mechanical platforms that enable multi-virus biosensing. The working mechanisms of each platform, including the detection principle, transducer configuration, bio-interface design, and detected signals, are reviewed. The advantages and limitations, as well as the challenges in implementing various detection strategies in real-life scenarios, were evaluated. Future perspectives on multiplexed biosensing techniques are critically discussed. Earlier access to multi-virus biosensors will efficiently serve for immediate pandemic control, such as in emerging SARS-CoV-2 and monkeypox cases.
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Affiliation(s)
- Yuqiao Zheng
- Zhejiang University, Hangzhou, 310058, Zhejiang, China,Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Xixi Song
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Zina Fredj
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Sumin Bian
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China.
| | - Mohamad Sawan
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China.
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Wang C, Fang C, Zou Y, Yang J, Sawan M. Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction. J Neural Eng 2023; 20. [PMID: 36634357 DOI: 10.1088/1741-2552/acb295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023]
Abstract
Objective. Retinal prostheses are promising devices to restore vision for patients with severe age-related macular degeneration or retinitis pigmentosa disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina's working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina's working principle and state-of-the-arts computer vision models are combined together.Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models.Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect.Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What's more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.
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Affiliation(s)
- Chuanqing Wang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
| | - Chaoming Fang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
| | - Yong Zou
- Beijing Institute of Radiation Medicine, Beijing, People's Republic of China
| | - Jie Yang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
| | - Mohamad Sawan
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
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El-Sayegh B, Cacciari LP, Primeau FL, Sawan M, Dumoulin C. The state of pelvic floor muscle dynamometry: A scoping review. Neurourol Urodyn 2023; 42:478-499. [PMID: 36478202 DOI: 10.1002/nau.25101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 10/01/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
AIMS To discuss the advantages and limitation of the different pelvic floor muscle (PFM) dynamometers available, both in research and industry, and to present the extent of variation between them in terms of structure, functioning, psychometric properties, and assessment procedures. METHODS We identified relevant studies from four databases (MEDLINE, Compendex, Web of Science, and Derwent Innovations Index) up to December 2020 using terms related to dynamometry and PFM. In addition, we conducted a hand search of the bibliographies of all relevant reports. Peer-reviewed papers, conference proceedings, patents and user's manuals for commercial dynamometers were included and assessed by two independent reviewers. RESULTS One hundred and one records were included and 23 PFM dynamometers from 15 research groups were identified. From these, 20 were considered as clinical dynamometers (meant for research settings) and three as personal dynamometers (developed by the industry). Overall, significant heterogeneity was found in their structure and functioning, which limits development of normative data for PFM force in women. Further research is needed to assess the psychometric properties of PFM dynamometers and to standardize assessment procedures. CONCLUSION This review points up to the heterogeneity of existing dynamometers and methods of assessing PFM function. It highlights the need to better document their design and assessment protocol methods. Additionally, this review recommends standards for new dynamometers to allow the establishment of normalized data.
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Affiliation(s)
- Batoul El-Sayegh
- Department of Electrical and Computer Engineering, Polytechnique of Montreal, Montreal, Québec, Canada.,Research Center of the Institut Universtaire de Gériatrie de Montréal, Montréal, Québec, Canada
| | - Licia P Cacciari
- Research Center of the Institut Universtaire de Gériatrie de Montréal, Montréal, Québec, Canada.,School of Rehabilitation, Université de Montréal, Montréal, Québec, Canada
| | - Francois L Primeau
- Department of Electrical and Computer Engineering, Polytechnique of Montreal, Montreal, Québec, Canada
| | - Mohamad Sawan
- School of Engineering, Westlake University and Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, China
| | - Chantal Dumoulin
- Research Center of the Institut Universtaire de Gériatrie de Montréal, Montréal, Québec, Canada.,School of Rehabilitation, Université de Montréal, Montréal, Québec, Canada
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26
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Fredj Z, Sawan M. Advanced Nanomaterials-Based Electrochemical Biosensors for Catecholamines Detection: Challenges and Trends. Biosensors (Basel) 2023; 13:211. [PMID: 36831978 PMCID: PMC9953752 DOI: 10.3390/bios13020211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Catecholamines, including dopamine, epinephrine, and norepinephrine, are considered one of the most crucial subgroups of neurotransmitters in the central nervous system (CNS), in which they act at the brain's highest levels of mental function and play key roles in neurological disorders. Accordingly, the analysis of such catecholamines in biological samples has shown a great interest in clinical and pharmaceutical importance toward the early diagnosis of neurological diseases such as Epilepsy, Parkinson, and Alzheimer diseases. As promising routes for the real-time monitoring of catecholamine neurotransmitters, optical and electrochemical biosensors have been widely adopted and perceived as a dramatically accelerating development in the last decade. Therefore, this review aims to provide a comprehensive overview on the recent advances and main challenges in catecholamines biosensors. Particular emphasis is given to electrochemical biosensors, reviewing their sensing mechanism and the unique characteristics brought by the emergence of nanotechnology. Based on specific biosensors' performance metrics, multiple perspectives on the therapeutic use of nanomaterial for catecholamines analysis and future development trends are also summarized.
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Tian F, Yang J, Zhao S, Sawan M. NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications. Front Neurosci 2023; 17:1093865. [PMID: 36755733 PMCID: PMC9900119 DOI: 10.3389/fnins.2023.1093865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/03/2023] [Indexed: 01/24/2023] Open
Abstract
Highly accurate classification methods for multi-task biomedical signal processing are reported, including neural networks. However, reported works are computationally expensive and power-hungry. Such bottlenecks make it hard to deploy existing approaches on edge platforms such as mobile and wearable devices. Gaining motivation from the good performance and high energy-efficiency of spiking neural networks (SNNs), a generic neuromorphic framework for edge healthcare and biomedical applications are proposed and evaluated on various tasks, including electroencephalography (EEG) based epileptic seizure prediction, electrocardiography (ECG) based arrhythmia detection, and electromyography (EMG) based hand gesture recognition. This approach, NeuroCARE, uses a unique sparse spike encoder to generate spike sequences from raw biomedical signals and makes classifications using the spike-based computing engine that combines the advantages of both CNN and SNN. An adaptive weight mapping method specifically co-designed with the spike encoder can efficiently convert CNN to SNN without performance deterioration. The evaluation results show that the overall performance, including the classification accuracy, sensitivity and F1 score, achieve 92.7, 96.7, and 85.7% for seizure prediction, arrhythmia detection and hand gesture recognition, respectively. In comparison with CNN topologies, the computation complexity is reduced by over 80.7% while the energy consumption and area occupation are reduced by over 80% and over 64.8%, respectively, indicating that the proposed neuromorphic computing approach is energy and area efficient and of high precision, which paves the way for deployment at edge platforms.
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Affiliation(s)
- Fengshi Tian
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, Zhejiang, China,The Hong Kong University of Science and Technology (HKUST), New Territories, Hong Kong SAR, China
| | - Jie Yang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, Zhejiang, China,*Correspondence: Jie Yang,
| | - Shiqi Zhao
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, Zhejiang, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, Zhejiang, China,Mohamad Sawan,
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Gagliano L, Ding TY, Toffa DH, Beauregard L, Robert M, Lesage F, Sawan M, Nguyen DK, Bou Assi E. Decrease in wearable-based nocturnal sleep efficiency precedes epileptic seizures. Front Neurol 2023; 13:1089094. [PMID: 36712456 PMCID: PMC9875007 DOI: 10.3389/fneur.2022.1089094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
Introduction While it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer. Methods Nocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not. Results Lower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days (p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset (p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001). Discussion Findings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy.
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Affiliation(s)
- Laura Gagliano
- Institute of Biomedical Engineering and the Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada,Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada,*Correspondence: Laura Gagliano ✉
| | - Tian Yue Ding
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Denahin H. Toffa
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Laurence Beauregard
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Manon Robert
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering and the Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada
| | - Mohamad Sawan
- Institute of Biomedical Engineering and the Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada,CenBRAIN, Westlake University, Hangzhou, China
| | - Dang K. Nguyen
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada,Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
| | - Elie Bou Assi
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada,Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
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Wu D, Shi Y, Wang Z, Yang J, Sawan M. C 2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction. IEEE Trans Neural Syst Rehabil Eng 2023; PP. [PMID: 37018579 DOI: 10.1109/tnsre.2023.3235390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Recent developments in brain-machine inter-face technology have rendered seizure prediction possible. However, the transmission of a large volume of electro-physiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose C2SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.
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Affiliation(s)
- Di Wu
- Zhejiang University, Hangzhou, China
| | - Yi Shi
- School of Engineering, Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), Westlake University, Hangzhou, China
| | - Ziyu Wang
- School of Engineering, Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), Westlake University, Hangzhou, China
| | - Jie Yang
- School of Engineering, Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), Westlake University, Hangzhou, China
| | - Mohamad Sawan
- School of Engineering, Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), Westlake University, Hangzhou, China
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31
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Chen YH, Yang J, Wu H, Beier KT, Sawan M. Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction. Front Psychiatry 2023; 14:1085036. [PMID: 36911117 PMCID: PMC9995819 DOI: 10.3389/fpsyt.2023.1085036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.
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Affiliation(s)
- Yun-Hsuan Chen
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jie Yang
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kevin T Beier
- Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, United States.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
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El-Sayegh B, Dumoulin C, Ali M, Assaf H, De Jong J, Sawan M, Leduc-Primeau F. Portable Dynamometer-Based Measurement of Pelvic Floor Muscle Force. IEEE J Transl Eng Health Med 2022; 11:44-53. [PMID: 36518785 PMCID: PMC9744264 DOI: 10.1109/jtehm.2022.3223258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/29/2022] [Accepted: 11/01/2022] [Indexed: 11/19/2022]
Abstract
OBJECTIVE In attempts to improve the quality of life of women, continuous projects are sought between rehabilitation intervention and engineering. Using the knowledge of the pelvic floor muscle (PFM) physiology, assessment and training methods are developed to reduce lower urinary tract symptoms such as urinary incontinence. Therefore, this paper covers the design and implementation of a portable vaginal dynamometer. METHODS A PFM probe is designed, 3D printed, assembled, and tested in ten women to assess its acceptability and usability. The feedback from the usability study is used to optimize the PFM probe design. A vaginal dynamometer is developed based on the designed PFM probe, then tested for linearity, repeatability, hysteresis, noise and heat effect, and power consumption. The variability between the different produced PFM probe prototypes is evaluated. RESULTS Force measurements are made using a load cell. Wireless communication is performed through a Bluetooth low energy transceiver v5.0, with a corresponding interface on both computer and smartphone. The device operates at a 3.3V supply and achieves a power consumption of 49.5 mW in operating mode. Two PFM probe sizes are designed to accommodate different vaginal hiatus sizes, based on usability study feedback. The proposed system allows the physiotherapist to wirelessly monitor variation in pelvic floor muscle force during assessment and/or training. DISCUSSION/CONCLUSION The testing results showed that the newly designed system has the potential to measure the PFM function in functional conditions such as the standing position.
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Affiliation(s)
- Batoul El-Sayegh
- Department of Electrical EngineeringPolytechnique Montreal Montreal QC H3T 1J4 Canada
- Research CenterInstitut Universtaire de Gériatrie de Montréal Montréal QC H3W 1W4 Canada
| | - Chantale Dumoulin
- Research CenterInstitut Universtaire de Gériatrie de Montréal Montréal QC H3W 1W4 Canada
- School of Rehabilitation, Faculty of MedicineUniversité de Montréal Montréal QC H3N 1X7 Canada
| | - Mohamed Ali
- Department of Electrical EngineeringPolytechnique Montreal Montreal QC H3T 1J4 Canada
- Department of MicroelectronicsElectronics Research Institute Cairo 12622 Egypt
| | - Hussein Assaf
- Department of Electrical EngineeringPolytechnique Montreal Montreal QC H3T 1J4 Canada
| | | | - Mohamad Sawan
- Department of Electrical EngineeringPolytechnique Montreal Montreal QC H3T 1J4 Canada
- School of EngineeringWestlake University and CenBRAIN Neurotech Center of Excellence, Westlake Institute for Advanced Study Hangzhou 310024 China
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Rong G, Zheng Y, Li X, Guo M, Su Y, Bian S, Dang B, Chen Y, Zhang Y, Shen L, Jin H, Yan R, Wen L, Zhu P, Sawan M. A high-throughput fully automatic biosensing platform for efficient COVID-19 detection. Biosens Bioelectron 2022; 220:114861. [PMCID: PMC9630290 DOI: 10.1016/j.bios.2022.114861] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 09/19/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
We propose a label-free biosensor based on a porous silicon resonant microcavity and localized surface plasmon resonance. The biosensor detects SARS-CoV-2 antigen based on engineered trimeric angiotensin converting enzyme-2 binding protein, which is conserved across different variants. Robotic arms run the detection process including sample loading, incubation, sensor surface rinsing, and optical measurements using a portable spectrometer. Both the biosensor and the optical measurement system are readily scalable to accommodate testing a wide range of sample numbers. The limit of detection is 100 TCID50/ml. The detection time is 5 min, and the throughput of one single robotic site is up to 384 specimens in 30 min. The measurement interface requires little training, has standard operation, and therefore is suitable for widespread use in rapid and onsite COVID-19 screening or surveillance.
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Affiliation(s)
- Guoguang Rong
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China
| | - Yuqiao Zheng
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China
| | - Xiangqing Li
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China
| | - Mengzhun Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake, University, Hangzhou, Zhejiang, 310030, China,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310030, China,Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310030, China
| | - Yi Su
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China
| | - Sumin Bian
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China
| | - Bobo Dang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake, University, Hangzhou, Zhejiang, 310030, China,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310030, China,Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310030, China
| | - Yin Chen
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Hangzhou, Zhejiang, 310051, China
| | - Yanjun Zhang
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Hangzhou, Zhejiang, 310051, China
| | - Linhai Shen
- Hangzhou Center for Disease Control and Prevention, 568 Mingshi Road, Jianggan District, Hangzhou, Zhejiang, 310021, China
| | - Hui Jin
- Hangzhou Center for Disease Control and Prevention, 568 Mingshi Road, Jianggan District, Hangzhou, Zhejiang, 310021, China
| | - Renhong Yan
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake, University, Hangzhou, Zhejiang, 310030, China,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310030, China
| | - Liaoyong Wen
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China
| | - Peixi Zhu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang, 310014, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China,Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, 310024, China,Corresponding author. CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang, 310030, China
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Rong G, Zheng Y, Yang X, Bao K, Xia F, Ren H, Bian S, Li L, Zhu B, Sawan M. A Closed-Loop Approach to Fight Coronavirus: Early Detection and Subsequent Treatment. Biosensors (Basel) 2022; 12:900. [PMID: 36291037 PMCID: PMC9599914 DOI: 10.3390/bios12100900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The recent COVID-19 pandemic has caused tremendous damage to the social economy and people's health. Some major issues fighting COVID-19 include early and accurate diagnosis and the shortage of ventilator machines for critical patients. In this manuscript, we describe a novel solution to deal with COVID-19: portable biosensing and wearable photoacoustic imaging for early and accurate diagnosis of infection and magnetic neuromodulation or minimally invasive electrical stimulation to replace traditional ventilation. The solution is a closed-loop system in that the three modules are integrated together and form a loop to cover all-phase strategies for fighting COVID-19. The proposed technique can guarantee ubiquitous and onsite detection, and an electrical hypoglossal stimulator can be more effective in helping severe patients and reducing complications caused by ventilators.
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Affiliation(s)
- Guoguang Rong
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Yuqiao Zheng
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Xi Yang
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Kangjian Bao
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Fen Xia
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Huihui Ren
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Sumin Bian
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Lan Li
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Bowen Zhu
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China
- Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China
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Zhang Y, Savaria Y, Zhao S, Mordido G, Sawan M, Leduc-Primeau F. Tiny CNN for Seizure Prediction in Wearable Biomedical Devices. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:1306-1309. [PMID: 36086510 DOI: 10.1109/embc48229.2022.9872006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Epilepsy is a life-threatening disease affecting millions of people all over the world. Artificial intelligence epileptic predictors offer excellent potential to improve epilepsy therapy. Particularly, deep learning models such as convolutional neural networks (CNN) can be used to accurately detect ictogenesis through deep structured learning representations. In this work, a tiny one-dimensional stacked convolutional neural network (1DSCNN) is proposed based on short-time Fourier transform (STFT) to predict epileptic seizure. The results demonstrate that the proposed method obtains better performance compared to recent state-of-the-art methods, achieving an average sensitivity of 94.44%, average false prediction rate (FPR) of 0.011/h and average area under the curve (AUC) of 0.979 on the test set of the American Epilepsy Society Seizure Prediction Challenge dataset, while featuring a model size of only 21.32kB. Furthermore, after adapting the model to 4-bit quantization, its size is significantly decreased by 7.08x with only 0.51% AUC score precision loss, which shows excellent potential for hardware-friendly wearable implementation.
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Wang J, Chen YH, Yang J, Sawan M. Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern. Biosensors 2022; 12:bios12060384. [PMID: 35735532 PMCID: PMC9221354 DOI: 10.3390/bios12060384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 11/16/2022]
Abstract
To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.
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Affiliation(s)
- Jiachen Wang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
| | - Yun-Hsuan Chen
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
- Correspondence: (Y.-H.C.); (M.S.)
| | - Jie Yang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Mohamad Sawan
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
- Correspondence: (Y.-H.C.); (M.S.)
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Wu D, Yang J, Sawan M. Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation. J Neural Eng 2022; 19. [PMID: 35617933 DOI: 10.1088/1741-2552/ac73b3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/26/2022] [Indexed: 11/11/2022]
Abstract
Deep neural networks (DNN) have shown unprecedented success in various brain-machine interface (BMI) applications such as epileptic seizure prediction. However, existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. Therefore, only a limited number of labeled recordings from each subject can be used for training. As a consequence, current DNN based methods demonstrate poor generalization ability to some extent due to the insufficiency of training data. On the other hand, patient-independent models attempt to utilize more patient data to train a universal model for all patients by pooling patient data together. Despite different techniques applied, results show that patient-independent models perform worse than patient-specific models due to high individual variation across patients. A substantial gap thus exists between patient-specific and patient-independent models. In this paper, we propose a novel training scheme based on knowledge distillation which makes use of a large amount of data from multiple subjects. It first distills informative features from signals of all available subjects with a pre-trained general model. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors. Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme. The resulting accuracy, sensitivity, and false prediction rate show that our proposed training scheme consistently improves the prediction performance of state-of-the-art methods by a large margin.
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Affiliation(s)
- Di Wu
- Westlake University, Westlake University, Hangzhou, 310024, China, Hangzhou, 310024, CHINA
| | - Jie Yang
- Westlake University, Westlake University, Hangzhou, 310024, China, Hangzhou, 310024, CHINA
| | - Mohamad Sawan
- Westlake University, Westlake University, Hangzhou, 310024, China, Hangzhou, 310024, CHINA
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Abstract
Epilepsy is a chronic neurological disorder affecting 1% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for accurate epileptic seizure (ES) prediction, thereby benefiting patients suffering from epilepsy. To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms. However, the scarcity of seizure onsets leads to significant insufficiency of data for training the DL algorithms. To overcome this data insufficiency, in this paper, we propose a preictal artificial signal synthesis algorithm based on a generative adversarial network to generate synthetic multichannel EEG preictal samples. A high-quality single-channel architecture, determined by visual and statistical evaluations, is used to train the generators of multichannel samples. The effectiveness of the synthetic samples is evaluated by comparing the ES prediction performances without and with synthetic preictal sample augmentation. The leave-one-seizure-out cross validation ES prediction accuracy and corresponding area under the receiver operating characteristic curve evaluation improve from 73.0% and 0.676 to 78.0% and 0.704 by 10x synthetic sample augmentation, respectively. The obtained results indicate that synthetic preictal samples are effective for enhancing ES prediction performance.
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Wang C, Yang J, Sawan M. NeuroSEE: A Neuromorphic Energy Efficient Processing Framework for Visual Prostheses. IEEE J Biomed Health Inform 2022; 26:4132-4141. [PMID: 35503849 DOI: 10.1109/jbhi.2022.3172306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Visual prostheses with both comprehensive visual signal processing capability and energy efficiency are becoming increasingly demanded in the age of intelligent personal healthcare, particularly with the rise of wearable and implantable devices. To address this trend, we propose NeuroSEE, a neuromorphic energy-efficient processing framework that combines a spike representation encoding technique and a bio-inspired processing method. This framework first utilizes sparse spike trains to represent visual information, and then a bio-inspired spiking neural network (SNN) is adopted to process the spike trains. The SNN model makes use of an IF neuron with multiple spikefiring rates to decrease the energy consumption without compensating for prediction performance. The experimental results indicate that when predicting the response of the primary visual cortex, the framework achieves a state-ofthe- art Pearson correlation coefficient performance. Spikebased recording and processing methods simplify the storage and transmission of redundant scene information and complex calculation processes. It could reduce power consumption by 15 times compared with the existing Convolutional neural network (CNN) processing framework. The proposed NeuroSEE framework predicts the response of the primary visual cortex in an energy efficient manner, making it a powerful tool for visual prostheses.
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Azizipour N, Avazpour R, Sawan M, Ajji A, H Rosenzweig D. Surface Optimization and Design Adaptation toward Spheroid Formation On-Chip. Sensors (Basel) 2022; 22:s22093191. [PMID: 35590879 DOI: 10.1039/d2sd00004k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/07/2022] [Accepted: 04/19/2022] [Indexed: 05/27/2023]
Abstract
Spheroids have become an essential tool in preclinical cancer research. The uniformity of spheroids is a critical parameter in drug test results. Spheroids form by self-assembly of cells. Hence, the control of homogeneity of spheroids in terms of size, shape, and density is challenging. We developed surface-optimized polydimethylsiloxane (PDMS) biochip platforms for uniform spheroid formation on-chip. These biochips were surface modified with 10% bovine serum albumin (BSA) to effectively suppress cell adhesion on the PDMS surface. These surface-optimized platforms facilitate cell self-aggregations to produce homogenous non-scaffold-based spheroids. We produced uniform spheroids on these biochips using six different established human cell lines and a co-culture model. Here, we observe that the concentration of the BSA is important in blocking cell adhesion to the PDMS surfaces. Biochips treated with 3% BSA demonstrated cell repellent properties similar to the bare PDMS surfaces. This work highlights the importance of surface modification on spheroid production on PDMS-based microfluidic devices.
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Affiliation(s)
- Neda Azizipour
- Institut de Génie Biomédical, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada
| | - Rahi Avazpour
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada
| | - Mohamad Sawan
- Institut de Génie Biomédical, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada
- Polystim Neurotech Laboratory, Electrical Engineering Department, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
- CenBRAIN Laboratory, Westlake Institute for Advanced Study, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Abdellah Ajji
- Institut de Génie Biomédical, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada
- The Research Center for High Performance Polymer and Composite Systems, Chemical Engineering Department, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada
| | - Derek H Rosenzweig
- Department of Surgery, McGill University, Montréal, QC H3G 1A4, Canada
- Injury, Repair and Recovery Program, Research Institute of McGill University Health Centre, Montréal, QC H3H 2R9, Canada
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Azizipour N, Avazpour R, Sawan M, Ajji A, H. Rosenzweig D. Surface Optimization and Design Adaptation toward Spheroid Formation On-Chip. Sensors (Basel) 2022; 22:s22093191. [PMID: 35590879 PMCID: PMC9104470 DOI: 10.3390/s22093191] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/07/2022] [Accepted: 04/19/2022] [Indexed: 12/17/2022]
Abstract
Spheroids have become an essential tool in preclinical cancer research. The uniformity of spheroids is a critical parameter in drug test results. Spheroids form by self-assembly of cells. Hence, the control of homogeneity of spheroids in terms of size, shape, and density is challenging. We developed surface-optimized polydimethylsiloxane (PDMS) biochip platforms for uniform spheroid formation on-chip. These biochips were surface modified with 10% bovine serum albumin (BSA) to effectively suppress cell adhesion on the PDMS surface. These surface-optimized platforms facilitate cell self-aggregations to produce homogenous non-scaffold-based spheroids. We produced uniform spheroids on these biochips using six different established human cell lines and a co-culture model. Here, we observe that the concentration of the BSA is important in blocking cell adhesion to the PDMS surfaces. Biochips treated with 3% BSA demonstrated cell repellent properties similar to the bare PDMS surfaces. This work highlights the importance of surface modification on spheroid production on PDMS-based microfluidic devices.
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Affiliation(s)
- Neda Azizipour
- Institut de Génie Biomédical, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada; (N.A.); (M.S.)
| | - Rahi Avazpour
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada;
| | - Mohamad Sawan
- Institut de Génie Biomédical, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada; (N.A.); (M.S.)
- Polystim Neurotech Laboratory, Electrical Engineering Department, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
- CenBRAIN Laboratory, Westlake Institute for Advanced Study, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Abdellah Ajji
- Institut de Génie Biomédical, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada; (N.A.); (M.S.)
- The Research Center for High Performance Polymer and Composite Systems, Chemical Engineering Department, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada
- Correspondence: (A.A.); (D.H.R.)
| | - Derek H. Rosenzweig
- Department of Surgery, McGill University, Montréal, QC H3G 1A4, Canada
- Injury, Repair and Recovery Program, Research Institute of McGill University Health Centre, Montréal, QC H3H 2R9, Canada
- Correspondence: (A.A.); (D.H.R.)
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Zheng Y, Bian S, Sun J, Wen L, Rong G, Sawan M. Label-Free LSPR-Vertical Microcavity Biosensor for On-Site SARS-CoV-2 Detection. Biosensors (Basel) 2022; 12:bios12030151. [PMID: 35323421 PMCID: PMC8946032 DOI: 10.3390/bios12030151] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/18/2022] [Accepted: 02/24/2022] [Indexed: 05/08/2023]
Abstract
Cost-effective, rapid, and sensitive detection of SARS-CoV-2, in high-throughput, is crucial in controlling the COVID-19 epidemic. In this study, we proposed a vertical microcavity and localized surface plasmon resonance hybrid biosensor for SARS-CoV-2 detection in artificial saliva and assessed its efficacy. The proposed biosensor monitors the valley shifts in the reflectance spectrum, as induced by changes in the refractive index within the proximity of the sensor surface. A low-cost and fast method was developed to form nanoporous gold (NPG) with different surface morphologies on the vertical microcavity wafer, followed by immobilization with the SARS-CoV-2 antibody for capturing the virus. Modeling and simulation were conducted to optimize the microcavity structure and the NPG parameters. Simulation results revealed that NPG-deposited sensors performed better in resonance quality and in sensitivity compared to gold-deposited and pure microcavity sensors. The experiment confirmed the effect of NPG surface morphology on the biosensor sensitivity as demonstrated by simulation. Pre-clinical validation revealed that 40% porosity led to the highest sensitivity for SARS-CoV-2 pseudovirus at 319 copies/mL in artificial saliva. The proposed automatic biosensing system delivered the results of 100 samples within 30 min, demonstrating its potential for on-site coronavirus detection with sufficient sensitivity.
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Affiliation(s)
- Yuqiao Zheng
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou 310024, China; (Y.Z.); (S.B.)
| | - Sumin Bian
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou 310024, China; (Y.Z.); (S.B.)
| | - Jiacheng Sun
- School of Engineering, Westlake University, Hangzhou 310024, China; (J.S.); (L.W.)
| | - Liaoyong Wen
- School of Engineering, Westlake University, Hangzhou 310024, China; (J.S.); (L.W.)
| | - Guoguang Rong
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou 310024, China; (Y.Z.); (S.B.)
- Correspondence: (G.R.); (M.S.)
| | - Mohamad Sawan
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou 310024, China; (Y.Z.); (S.B.)
- Correspondence: (G.R.); (M.S.)
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Zhang H, Rong G, Bian S, Sawan M. Lab-on-Chip Microsystems for Ex Vivo Network of Neurons Studies: A Review. Front Bioeng Biotechnol 2022; 10:841389. [PMID: 35252149 PMCID: PMC8888888 DOI: 10.3389/fbioe.2022.841389] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
Increasing population is suffering from neurological disorders nowadays, with no effective therapy available to treat them. Explicit knowledge of network of neurons (NoN) in the human brain is key to understanding the pathology of neurological diseases. Research in NoN developed slower than expected due to the complexity of the human brain and the ethical considerations for in vivo studies. However, advances in nanomaterials and micro-/nano-microfabrication have opened up the chances for a deeper understanding of NoN ex vivo, one step closer to in vivo studies. This review therefore summarizes the latest advances in lab-on-chip microsystems for ex vivo NoN studies by focusing on the advanced materials, techniques, and models for ex vivo NoN studies. The essential methods for constructing lab-on-chip models are microfluidics and microelectrode arrays. Through combination with functional biomaterials and biocompatible materials, the microfluidics and microelectrode arrays enable the development of various models for ex vivo NoN studies. This review also includes the state-of-the-art brain slide and organoid-on-chip models. The end of this review discusses the previous issues and future perspectives for NoN studies.
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Affiliation(s)
| | | | - Sumin Bian
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou, China
| | - Mohamad Sawan
- CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou, China
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Bian S, Shang M, Sawan M. Rapid biosensing SARS-CoV-2 antibodies in vaccinated healthy donors. Biosens Bioelectron 2022; 204:114054. [PMID: 35151002 PMCID: PMC8810518 DOI: 10.1016/j.bios.2022.114054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/23/2021] [Accepted: 01/27/2022] [Indexed: 01/06/2023]
Abstract
In this study, we report two fiber optic-biolayer interferometry (FO-BLI)-based biosensors for the rapid detection of SARS-CoV-2 neutralizing antibodies (NAbs) and binding antibodies (BAbs) in human serum. The use of signal enhancer 3,3′-diaminobenzidine enabled the detection of NAbs, anti-receptor binding domain (anti-RBD) BAbs, and anti-extracellular domain of spike protein (anti-S-ECD) BAbs up to as low as 10 ng/mL in both buffer and 100-fold diluted serum. NAbs and BAbs could be detected individually over 7.5 and 13 min, respectively, or simultaneously by prolonging the detection time of the former. The protocol for the detection of BAbs could be utilized for detection of the RBD-N501Y variant with equal sensitivity and speed. Results of the NAbs and the anti-RBD BAbs biosensors correlated well with those of the corresponding commercial assay kit. Clinical utility of the two FO-BLI biosensors were further validated using a small cohort of samples randomly taken from 16 enrolled healthy participants who received inactivated vaccines. Two potent serum antibodies were identified, which showed high neutralizing capacities toward RBD and pseudovirus. Overall, the rapid automated biosensors can be used for an individual sample measurement of NAbs and BAbs as well as for high-throughput analysis. The findings of this study would be useful in COVID-19 related studies in vaccine trials, research on dynamics of the immune response, and epidemiology studies.
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Affiliation(s)
- Sumin Bian
- CenBRAIN, School of Engineering, Westlake University, China
| | - Min Shang
- Dept. of Cardiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, China
| | - Mohamad Sawan
- CenBRAIN, School of Engineering, Westlake University, China.
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Hammoud A, Assaf H, Savaria Y, Nguyen DK, Sawan M. A Molecular Imprinted PEDOT CMOS Chip-Based Biosensor for Carbamazepine Detection. IEEE Trans Biomed Circuits Syst 2022; 16:15-23. [PMID: 34962875 DOI: 10.1109/tbcas.2021.3138942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A miniaturized biosensor for carbamazepine (CBZ) detection and quantification was designed, implemented and fabricated. The 1×1 mm2 CMOS chip was packaged and coupled with a 3-electrode electrochemical cell. A complete characterization of the sensor was conducted via two steps: 1) Molecular imprinting of PEDOT polymer sites by cyclic voltammetry (CV) on glassy carbon electrode (GCE) surfaces; and 2) Quantification of CBZ solutions through both CV, and a current peak detection circuitry. The proposed biosensor offered high-selectivity and high-sensitivity to CBZ molecules. Scanning electron microscopy (SEM) was utilized to validate the synthesis of the PEDOT chains. CBZ removal from the imprinted polymer was conducted through soaking the modified GCEs in acetonitrile (ACN). Extraction was then confirmed by ultraviolet-visible (UV-vis) spectroscopy and CV analyzing data from pre- and post-template extraction. Furthermore, in order to characterize the electrodes' response to CBZ levels in phosphate buffered solution (PBS) with [Fe(CN)6]3-/4- as a redox pair/mediator, CV and peak detection was conducted resulting in redox peak currents vs. CBZ concentration graphs. The limits of detection (LOD) and quantification (LOQ) were calculated to be 2.04 and 6.2 μg/mL respectively. Finally, selectivity towards CBZ was validated by studying the effect of valproic acid (VPA) and phenytoin (PHT) on the biosensor's performance. The proposed biosensor is highly sensitive and selective to CBZ molecules, simple to construct and easy to operate.
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Bian S, Tao Y, Zhu Z, Zhu P, Wang Q, Wu H, Sawan M. On-Site Biolayer Interferometry-Based Biosensing of Carbamazepine in Whole Blood of Epileptic Patients. Biosensors (Basel) 2021; 11:516. [PMID: 34940273 PMCID: PMC8699405 DOI: 10.3390/bios11120516] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 06/14/2023]
Abstract
On-site monitoring of carbamazepine (CBZ) that allows rapid, sensitive, automatic, and high-throughput detection directly from whole blood is of urgent demand in current clinical practice for precision medicine. Herein, we developed two types (being indirect vs. direct) of fiber-optic biolayer interferometry (FO-BLI) biosensors for on-site CBZ monitoring. The indirect FO-BLI biosensor preincubated samples with monoclonal antibodies towards CBZ (MA-CBZ), and the mixture competes with immobilized CBZ to bind towards MA-CBZ. The direct FO-BLI biosensor used sample CBZ and CBZ-horseradish peroxidase (CBZ-HRP) conjugate to directly compete for binding with immobilized MA-CBZ, followed by a metal precipitate 3,3'-diaminobenzidine to amplify the signals. Indirect FO-BLI detected CBZ within its therapeutic range and was regenerated up to 12 times with negligible baseline drift, but reported results in 25 min. However, Direct FO-BLI achieved CBZ detection in approximately 7.5 min, down to as low as 10 ng/mL, with good accuracy, specificity and negligible matric interference using a high-salt buffer. Validation of Direct FO-BLI using six paired sera and whole blood from epileptic patients showed excellent agreement with ultra-performance liquid chromatography. Being automated and able to achieve high throughput, Direct FO-BLI proved itself to be more effective for integration into the clinic by delivering CBZ values from whole blood within minutes.
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Affiliation(s)
- Sumin Bian
- CenBRAIN Labs, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Ying Tao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China; (Y.T.); (P.Z.)
| | - Zhoule Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China; (Z.Z.); (H.W.)
| | - Peixi Zhu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China; (Y.T.); (P.Z.)
| | - Qiqin Wang
- Institute of Pharmaceutical Analysis, College of Pharmacy, Jinan University, Guangzhou 510632, China;
| | - Hemmings Wu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China; (Z.Z.); (H.W.)
| | - Mohamad Sawan
- CenBRAIN Labs, School of Engineering, Westlake University, Hangzhou 310024, China
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Wang Z, Yang J, Wu H, Zhu J, Sawan M. Power efficient refined seizure prediction algorithm based on an enhanced benchmarking. Sci Rep 2021; 11:23498. [PMID: 34873202 PMCID: PMC8648730 DOI: 10.1038/s41598-021-02798-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 11/22/2021] [Indexed: 11/18/2022] Open
Abstract
Deep learning techniques have led to significant advancements in seizure prediction research. However, corresponding used benchmarks are not uniform in published results. Moreover, inappropriate training and evaluation processes used in various work create overfitted models, making prediction performance fluctuate or unreliable. In this study, we analyzed the various data preparation methods, dataset partition methods in related works, and explained the corresponding impacts to the prediction algorithms. Then we applied a robust processing procedure that considers the appropriate sampling parameters and the leave-one-out cross-validation method to avoid possible overfitting and provide prerequisites for ease benchmarking. Moreover, a deep learning architecture takes advantage of a one-dimension convolutional neural network and a bi-directional long short-term memory network is proposed for seizure prediction. The architecture achieves 77.6% accuracy, 82.7% sensitivity, and 72.4% specificity, and it outperforms the indicators of other prior-art works. The proposed model is also hardware friendly; it has 6.274 k parameters and requires only 12.825 M floating-point operations, which is advantageous for memory and power constrained device implementations.
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Affiliation(s)
- Ziyu Wang
- Cutting-Edge Net of Biomedical Research and INnovation (CenBRAIN), School of Engineering, Westlake University, Hangzhou, Zhejiang, China
| | - Jie Yang
- Cutting-Edge Net of Biomedical Research and INnovation (CenBRAIN), School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Junming Zhu
- Department of Neurosurgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mohamad Sawan
- Cutting-Edge Net of Biomedical Research and INnovation (CenBRAIN), School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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Wolf DC, Desgent S, Sanon NT, Chen JS, Elkaim LM, Bosoi CM, Awad PN, Simard A, Salam MT, Bilodeau GA, Duss S, Sawan M, Lewis EC, Weil AG. Sex differences in the developing brain impact stress-induced epileptogenicity following hyperthermia-induced seizures. Neurobiol Dis 2021; 161:105546. [PMID: 34742878 DOI: 10.1016/j.nbd.2021.105546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/19/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022] Open
Abstract
Febrile seizures (FS) are common, affecting 2-5% of children between the ages of 3 months and 6 years. Complex FS occur in 10% of patients with FS and are strongly associated with mesial temporal lobe epilepsy. Current research suggests that predisposing factors, such as genetic and anatomic abnormalities, may be necessary for complex FS to translate to mesial temporal lobe epilepsy. Sex hormones are known to influence seizure susceptibility and epileptogenesis, but whether sex-specific effects of early life stress play a role in epileptogenesis is unclear. Here, we investigate sex differences in the activity of the hypothalamic-pituitary-adrenal (HPA) axis following chronic stress and the underlying contributions of gonadal hormones to the susceptibility of hyperthermia-induced seizures (HS) in rat pups. Chronic stress consisted of daily injections of 40 mg/kg of corticosterone (CORT) subcutaneously from postnatal day (P) 1 to P9 in male and female rat pups followed by HS at P10. Body mass, plasma CORT levels, temperature threshold to HS, seizure characteristics, and electroencephalographic in vivo recordings were compared between CORT- and vehicle (VEH)-injected littermates during and after HS at P10. In juvenile rats (P18-P22), in vitro CA1 pyramidal cell recordings were recorded in males to investigate excitatory and inhibitory neuronal circuits. Results show that daily CORT injections increased basal plasma CORT levels before HS and significantly reduced weight gain and body temperature threshold of HS in both males and females. CORT also significantly lowered the generalized convulsions (GC) latency while increasing recovery time and the number of electrographic seizures (>10s), which had longer duration. Furthermore, sex-specific differences were found in response to chronic CORT injections. Compared to females, male pups had increased basal plasma CORT levels after HS, longer recovery time and a higher number of electrographic seizures (>10s), which also had longer duration. Sex-specific differences were also found at baseline conditions with lower latency to generalized convulsions and longer duration of electrographic seizures in males but not in females. In juvenile male rats, the amplitude of evoked excitatory postsynaptic potentials, as well as the amplitude of inhibitory postsynaptic currents, were significantly greater in CORT rats when compared to VEH littermates. These findings not only validate CORT injections as a stress model, but also show a sex difference in baseline conditions as well as a response to chronic CORT and an impact on seizure susceptibility, supporting a potential link between sustained early-life stress and complex FS. Overall, these effects also indicate a putatively less severe phenotype in female than male pups. Ultimately, studies investigating the biological underpinnings of sex differences as a determining factor in mental and neurologic problems are necessary to develop better diagnostic, preventative, and therapeutic approaches for all patients regardless of their sex.
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Affiliation(s)
- Daniele C Wolf
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada; Département de Neurosciences, Université de Montréal, Québec, Canada.
| | - Sébastien Desgent
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada; Département de Neurosciences, Université de Montréal, Québec, Canada
| | - Nathalie T Sanon
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada
| | - Jia-Shu Chen
- The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Lior M Elkaim
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Ciprian M Bosoi
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada
| | - Patricia N Awad
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada
| | - Alexe Simard
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada
| | - Muhammad T Salam
- Laboratoire Polystim, Département de génie électrique, Polytechnique Montréal, Montréal, Québec, Canada
| | - Guillaume-Alexandre Bilodeau
- LITIV Lab., Département de génie informatique et génie logiciel, Polytechnique Montréal, Montréal, Québec, Canada
| | - Sandra Duss
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada
| | - Mohamad Sawan
- Laboratoire Polystim, Département de génie électrique, Polytechnique Montréal, Montréal, Québec, Canada
| | | | - Alexander G Weil
- Centre de Recherche, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Département de Pédiatrie, Université de Montréal, Québec, Canada; Département de Neurosciences, Université de Montréal, Québec, Canada; Neurosurgery Service, Department of Surgery, Université de Montréal, Québec, Canada
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Champagne PO, Sanon NT, Carmant L, Nguyen DK, Deschênes S, Pouliot P, Bouthillier A, Sawan M. Superparamagnetic iron oxide nanoparticles-based detection of neuronal activity. Nanomedicine 2021; 40:102478. [PMID: 34743018 DOI: 10.1016/j.nano.2021.102478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 09/25/2021] [Accepted: 10/06/2021] [Indexed: 10/19/2022]
Abstract
Precise detection of brain regions harboring heightened electrical activity plays a central role in the understanding and treatment of diseases such as epilepsy. Superparamagnetic iron oxide nanoparticles (SPIONs) react to magnetic fields by aggregating and represent interesting candidates as new sensors for neuronal magnetic activity. We hypothesized that SPIONs in aqueous solution close to active brain tissue would aggregate proportionally to neuronal activity. We tested this hypothesis using an in vitro model of rat brain slice with different levels of activity. Aggregation was assessed with dynamic light scattering (DLS) and magnetic resonance imaging (MRI). We found that increasing brain slice activity was associated with higher levels of aggregation as measured by DLS and MRI, suggesting that the magnetic fields from neuronal tissue could induce aggregation in nearby SPIONs in solution. MRI signal change induced by SPIONs aggregation could serve as a powerful new tool for detection of brain electrical activity.
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Affiliation(s)
- Pierre-Olivier Champagne
- Polystim Neurotech Laboratory, Electrical Engineering Department, Polytechnique Montreal, Montreal, Canada; CHU Sainte-Justine Research Center, Montreal, Canada; Neurosurgery department, University of Montreal Medical Center, Montreal, Canada.
| | | | - Lionel Carmant
- CHU Sainte-Justine Research Center, Montreal, Canada; Neurology department, CHU Sainte-Justine, Montréal, Canada
| | - Dang Khoa Nguyen
- Neurology department, University of Montreal Medical Center, Montreal, Canada
| | | | - Philippe Pouliot
- Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada; Research Center, Montreal Heart Institute, Montreal, Canada
| | - Alain Bouthillier
- Neurosurgery department, University of Montreal Medical Center, Montreal, Canada
| | - Mohamad Sawan
- Polystim Neurotech Laboratory, Electrical Engineering Department, Polytechnique Montreal, Montreal, Canada; Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada
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Das PS, Gagnon-Turcotte G, Mascret Q, Bou Assi E, Toffa DH, Sawan M, Nguyen DK, Gosselin B. A versatile wearable sEMG recording system for long-term epileptic seizure monitoring. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:7489-7492. [PMID: 34892825 DOI: 10.1109/embc46164.2021.9629509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Surface electromyography (sEMG) can be used to detect motor epileptic seizures non-invasively. For clinical use, a compact-size, user-friendly, safe and accurate sEMG measurement system can be worn by epileptic patients to detect and characterize a seizure. Such devices must be small, wireless, power-efficient minimally invasive and robust to avoid movement artefacts, friction, and slipping of the electrode, which can compromise data integrity and/or generate false positives or false negatives. This paper presents a highly versatile device that can be worn in different locations on the body to capture sEMG signals in a freely moving user without movement artefact. The system can be safely worn on the body for several hours to capture sEMG from wet Ag/AgCl electrodes, while sEMG data is wirelessly transmitted to a host computer within a range of 20 m. We demonstrate the versatility of our sensor by recording sEMG from five different body locations in a freely moving volunteer. Then, simulated seizure data was captured while the device was placed on the extensor carpi ulnaris. We show that sEMG bursts were successfully recorded to characterize the seizure afterward. The presented sensor prototype is small (5 cm x 3.5 cm x 1 cm), lightweight (46 g), and has an autonomy of 12 hrs from a small 110-mAh battery.
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