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Lee J, Lee AH, Leung V, Laiwalla F, Lopez-Gordo MA, Larson L, Nurmikko A. An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors. NATURE ELECTRONICS 2024; 7:313-324. [PMID: 38737565 PMCID: PMC11078753 DOI: 10.1038/s41928-024-01134-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/15/2024] [Indexed: 05/14/2024]
Abstract
Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.
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Affiliation(s)
- Jihun Lee
- School of Engineering, Brown University, Providence, RI USA
| | - Ah-Hyoung Lee
- School of Engineering, Brown University, Providence, RI USA
| | - Vincent Leung
- Electrical and Computer Engineering, Baylor University, Waco, TX USA
| | - Farah Laiwalla
- School of Engineering, Brown University, Providence, RI USA
| | - Miguel Angel Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain
| | | | - Arto Nurmikko
- School of Engineering, Brown University, Providence, RI USA
- Carney Institute for Brain Science, Brown University, Providence, RI USA
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Donati E, Valle G. Neuromorphic hardware for somatosensory neuroprostheses. Nat Commun 2024; 15:556. [PMID: 38228580 PMCID: PMC10791662 DOI: 10.1038/s41467-024-44723-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.
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Affiliation(s)
- Elisa Donati
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Giacomo Valle
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
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Tang X, Renteria-Pinon M, Tang W. Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1342-1354. [PMID: 37463086 DOI: 10.1109/tbcas.2023.3296529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.
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Chang EH, Gabalski AH, Huerta TS, Datta-Chaudhuri T, Zanos TP, Zanos S, Grill WM, Tracey KJ, Al-Abed Y. The Fifth Bioelectronic Medicine Summit: today's tools, tomorrow's therapies. Bioelectron Med 2023; 9:21. [PMID: 37794457 PMCID: PMC10552422 DOI: 10.1186/s42234-023-00123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 09/04/2023] [Indexed: 10/06/2023] Open
Abstract
The emerging field of bioelectronic medicine (BEM) is poised to make a significant impact on the treatment of several neurological and inflammatory disorders. With several BEM therapies being recently approved for clinical use and others in late-phase clinical trials, the 2022 BEM summit was a timely scientific meeting convening a wide range of experts to discuss the latest developments in the field. The BEM Summit was held over two days in New York with more than thirty-five invited speakers and panelists comprised of researchers and experts from both academia and industry. The goal of the meeting was to bring international leaders together to discuss advances and cultivate collaborations in this emerging field that incorporates aspects of neuroscience, physiology, molecular medicine, engineering, and technology. This Meeting Report recaps the latest findings discussed at the Meeting and summarizes the main developments in this rapidly advancing interdisciplinary field. Our hope is that this Meeting Report will encourage researchers from academia and industry to push the field forward and generate new multidisciplinary collaborations that will form the basis of new discoveries that we can discuss at the next BEM Summit.
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Affiliation(s)
- Eric H Chang
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA.
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA.
- The Elmezzi Graduate School of Molecular Medicine, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA.
| | - Arielle H Gabalski
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
| | - Tomas S Huerta
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Timir Datta-Chaudhuri
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
- The Elmezzi Graduate School of Molecular Medicine, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Theodoros P Zanos
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
- The Elmezzi Graduate School of Molecular Medicine, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Stavros Zanos
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
- The Elmezzi Graduate School of Molecular Medicine, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Fitzpatrick CIEMAS, Duke University, Room 1427, 101 Science Drive, Box 90281, Durham, NC, 27708, USA
| | - Kevin J Tracey
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
- The Elmezzi Graduate School of Molecular Medicine, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Yousef Al-Abed
- Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
- The Elmezzi Graduate School of Molecular Medicine, Northwell Health, 350 Community Drive, Manhasset, NY, 11030, USA
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