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Senthilkumar V, Thenmozhi S, Kumudavalli M, Yedukondalu U. Hybrid statistical and recurrent neural network architecture implementation in FPGA device used for severe acute respiratory syndrome coronavirus detector. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-224289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
The Severe Acute Respiratory Syndrome (SARS) are caused by the strain of the corona virus causes cold and influenza. In recent years, the covid pandemic spread throughout the world killing millions of people. The fatality rate has increased and it also leads to pneumonia for breathing problems. Several methods like wavelet filter banks, time series methods, Neural networks was developed for the diagnosis of severe acute respiratory syndrome coronavirus, still the accuracy can be improved. Less works is carried out for hardware implementation for syndrome detectors. This proposed work represents the FPGA (Field Programmable Gate Array) implementation of the hybrid method using Convolutional Recurrent neural network and Independent Components Analysis (ICA). The architecture extracts the ccomplex features from ECG (Electrocardiogram) samples. The hybrid Statistical and Recurrent Neural Network (RNN) Architecture implementation in a real time hardware detects the Severe Acute Respiratory Syndrome presented. The proposed method can be implemented in MATLAB, Embedded and DSP (Digital Signal Processor). But, the FPGAs consume less power computationally efficient. Since, ICA is an efficient method due to its blind source separation property accumulate the extraction of features accurate described. The mathematical model for the analysis of ECG signal using RNN is analyzed and based on that the proposed model is selected. On investigation the hybrid method using the statistical and neural network model is efficient in the analysis of biomedical signal especially ECG. The proposed ICA based RNN model is mathematically evaluated and tested with real time data. For implementation, Quartus software is used for effectiveness of the proposed model.
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Affiliation(s)
- V.M. Senthilkumar
- Department of ECE, Vivekanandha College of Engineering for Women (Autonomous), Namakkal, Tamilnadu, India
| | - S. Thenmozhi
- Department of ECE, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
| | - M.V. Kumudavalli
- Department of Computer Applications, Dayananda Sagar College of Arts Science and Commerce, Bangalore, Karnataka, India
| | - U. Yedukondalu
- Department of ECE, MVR College of Engineering & Technology (Autonomous) Paritala, Andhra Pradesh, India
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Internet of Low-Altitude UAVs (IoLoUA): a methodical modeling on integration of Internet of “Things” with “UAV” possibilities and tests. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10225-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Li J, Tobore I, Liu Y, Kandwal A, Wang L, Nie Z. Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN. IEEE J Biomed Health Inform 2021; 25:3340-3350. [PMID: 33848252 DOI: 10.1109/jbhi.2021.3072628] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications.
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A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia. SENSORS 2021; 21:s21155222. [PMID: 34372459 PMCID: PMC8348396 DOI: 10.3390/s21155222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 02/01/2023]
Abstract
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.
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Habibzadeh H, Dinesh K, Shishvan OR, Boggio-Dandry A, Sharma G, Soyata T. A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective. IEEE INTERNET OF THINGS JOURNAL 2020; 7:53-71. [PMID: 33748312 PMCID: PMC7970885 DOI: 10.1109/jiot.2019.2946359] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.
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Affiliation(s)
- Hadi Habibzadeh
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627
| | - Omid Rajabi Shishvan
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Andrew Boggio-Dandry
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627
| | - Tolga Soyata
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
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Wang LH, Zhang W, Guan MH, Jiang SY, Fan MH, Abu PAR, Chen CA, Chen SL. A Low-Power High-Data-Transmission Multi-Lead ECG Acquisition Sensor System. SENSORS 2019; 19:s19224996. [PMID: 31744095 PMCID: PMC6891589 DOI: 10.3390/s19224996] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/03/2019] [Accepted: 11/13/2019] [Indexed: 12/03/2022]
Abstract
This study presents a low-power multi-lead wearable electrocardiogram (ECG) signal sensor system design that can simultaneously acquire the electrocardiograms from three leads, I, II, and V1. The sensor system includes two parts, an ECG test clothing with five electrode patches and an acquisition device. Compared with the traditional 12-lead wired ECG detection instrument, which limits patient mobility and needs medical staff assistance to acquire the ECG signal, the proposed vest-type ECG acquisition system is very comfortable and easy to use by patients themselves anytime and anywhere, especially for the elderly. The proposed study incorporates three methods to reduce the power consumption of the system by optimizing the micro control unit (MCU) working mode, adjusting the radio frequency (RF) parameters, and compressing the transmitted data. In addition, Huffman lossless coding is used to compress the transmitted data in order to increase the sampling rate of the acquisition system. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Finally, a series of tests were performed on the designed wearable ECG device. The results have demonstrated that the multi-lead wearable ECG device can collect, process, and transmit ECG data through Bluetooth technology. The ECG waveforms collected by the device are clear, complete, and can be displayed in real-time on a mobile phone. The sampling rate of the proposed wearable sensor system is 250 Hz per lead, which is dependent on the lossless compression scheme. The device achieves a compression ratio of 2.31. By implementing a low power design on the device, the resulting overall operational current of the device is reduced by 37.6% to 9.87 mA under a supply voltage of 2.1 V. The proposed vest-type multi-lead ECG acquisition device can be easily employed by medical staff for clinical diagnosis and is a suitable wearable device in monitoring and nursing the off-ward patients.
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Affiliation(s)
- Liang-Hung Wang
- Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350108, China; (W.Z.); (M.-H.G.); (S.-Y.J.)
- Correspondence: (L.-H.W.); (M.-H.F.); (S.-L.C.)
| | - Wei Zhang
- Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350108, China; (W.Z.); (M.-H.G.); (S.-Y.J.)
| | - Ming-Hui Guan
- Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350108, China; (W.Z.); (M.-H.G.); (S.-Y.J.)
| | - Su-Ya Jiang
- Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350108, China; (W.Z.); (M.-H.G.); (S.-Y.J.)
| | - Ming-Hui Fan
- Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350108, China; (W.Z.); (M.-H.G.); (S.-Y.J.)
- Correspondence: (L.-H.W.); (M.-H.F.); (S.-L.C.)
| | - Patricia Angela R. Abu
- Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines;
| | - Chiung-An Chen
- Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan;
| | - Shih-Lun Chen
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
- Correspondence: (L.-H.W.); (M.-H.F.); (S.-L.C.)
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Koya AM, Deepthi PP. Plug and play self-configurable IoT gateway node for telemonitoring of ECG. Comput Biol Med 2019; 112:103359. [PMID: 31394482 DOI: 10.1016/j.compbiomed.2019.103359] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 06/26/2019] [Accepted: 07/15/2019] [Indexed: 11/25/2022]
Abstract
In the era of IoT and hyperconnection, an efficient electrocardiogram (ECG) telemonitoring system in wireless body area network (WBAN) demands an easy to use, self-configurable, secure, plug and play system with minimum hardware and computational complexities. The compression and quantization parameters required for an efficient representation of ECG signal will vary from patient to patient, from lead to lead, and from time to time. To this end, we propose a compressed sensing based WBAN with self-configurable gateway node (CS-SCGN) using deterministic binary block diagonal (DBBD) measurement matrix. The self-configurability is brought in through a low complex method for adaptive tuning of parameters with a careful choice of measurement matrix and data length. The redundant data transfer between sensor nodes and gateway node is avoided by addressing the diverse requirements in ECG remote health monitoring through three modes of configuration in the proposed system. A further reduction in communication and storage cost is achieved by optimizing the number of bits transmitted by sensor nodes by automatically tuning the compression ratio and quantization depth based on the dynamics of ECG signal. The self-configuration algorithm is designed to run at the gateway node in such a way as to optimize the power efficiency of sensor nodes without causing an extra power drain at the gateway node. Also, we investigate the feasibility of using smartphone as an IoT gateway node for performing primary processing to provide local utility before sending the received data to the remote server. The energy efficiency and real-time feasibility of the proposed algorithm are evaluated by implementing the gateway node on Odroid-XU4 board which runs on the same processor as in the latest smartphones. The experimental results indicate that our proposed self-configurable system at the gateway node makes the entire ECG telemonitoring system flexible, plug and play, patient independent and power-efficient.
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Affiliation(s)
- Aneesh M Koya
- Department of Electronics and Communication Engineering, National Institute of Technology, Calicut, Kerala, India.
| | - P P Deepthi
- Department of Electronics and Communication Engineering, National Institute of Technology, Calicut, Kerala, India
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Tiwari B, Tiwari V. An Intelligent Multi-Objective Framework of Pervasive Information Computing. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2018. [DOI: 10.4018/ijhisi.2018100102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article describes how electronic healthcare has been the key application of pervasive computing innovations to enhance healthcare quality and protect human lives. Specific patients of constant sicknesses and elderly individuals, by and large, may oblige continuous observing of their wellbeing status wherever they are. In this regard, remote patient monitoring technology plays the various important role through wearable devices to monitor patient's physiological figures. But, this must ensure some broad issues like, wearability, adaptability, interoperability, integration, security, and network efficiency. This article proposes a data-driven multi-layer architecture for pervasively remote patient monitoring that incorporates aforesaid issues. It enables the patient's care at the real time and supports anywhere and anytime requirement with using network infrastructure efficiently.
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Kumar A, Komaragiri R, Kumar M. Design of wavelet transform based electrocardiogram monitoring system. ISA TRANSACTIONS 2018; 80:381-398. [PMID: 30131166 DOI: 10.1016/j.isatra.2018.08.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 07/19/2018] [Accepted: 08/03/2018] [Indexed: 06/08/2023]
Abstract
The new age advancements in information technology due to materials and integrated circuit (IC) technologies and their applications in biomedical sciences have made the healthcare facilities more compact and affordable for the aging population. Market trends in healthcare and related devices indicate a sharp rise in their demand. Hence the researchers have converged the efforts on designing more smart and advanced medical devices using IC technology. Among these devices, cardiac pacemakers have become a recurrent biomedical device which is engrafted in the human body to detect and monitor a person's heart beating rate. The data thus generated is processed for various medical usages and devices via wireless methods. Cardiovascular diseases (CVDs) or diseases related to the heart are due to abnormalities or disorders of the heart and blood vessels. Till date, limited literature is available which focuses on a single technique that can perform all of the ECG signal denoising, ECG detection, lossless data compression and wireless transmission. In this work, a joint approach for denoising, detection, compression, and wireless transmission of ECG signal is proposed. The modified biorthogonal wavelet transform is used for denoising, detection and lossless compression of ECG signal. To reduce the circuit complexity, biorthogonal wavelet transform is realized using linear phase structure. Further, it is found in this work that the usage of modified biorthogonal wavelet transform increases the detection accuracy and CR of the proposed design. Also, in this work, the Wi-Fi-based wireless protocol is used for compressed data transmission. The proposed ECG detector achieves the highest sensitivity and positive predictivity of 99.95% and 99.92%, respectively, with the MIT-BIH arrhythmia database. The use of modified biorthogonal 3.1 wavelet transform and run-length encoding (RLE) for the compression of ECG data achieves a higher compression ratio (CR) of 6.271. To justify the effectiveness of the proposed algorithm, which uses modified biorthogonal wavelet 3.1transform, the results are compared with the existing methods, namely, Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.
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Affiliation(s)
- Ashish Kumar
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, Uttar Pradesh, 201310, India.
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, Uttar Pradesh, 201310, India.
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, Uttar Pradesh, 201310, India.
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Jegan R., Nimi W.S.. Sensor Based Smart Real Time Monitoring of Patients Conditions Using Wireless Protocol. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2018. [DOI: 10.4018/ijehmc.2018070105] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This article describes how physiological signal monitoring plays an important role in identifying the health condition of heart. In recent years, online monitoring and processing of biomedical signals play a major role in accurate clinical diagnosis. Therefore, there is a requirement for the developing of online monitoring systems that will be helpful for physicians to avoid mistakes. This article focuses on the method for real time acquisition of an ECG and PPG signal and it's processing and monitoring for tele-health applications. This article also presents the real time peak detection of ECG and PPG for vital parameters measurement. The implementation and design of the proposed wireless monitoring system can be done using a graphical programming environment that utilizes less power and a minimized area with reasonable speed. The advantages of the proposed work are very simple, low cost, easy integration with programming environment and continuous monitoring of physiological signals.
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Affiliation(s)
- Jegan R.
- Karunya University, Coimbatore, India
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Majumder S, Chen L, Marinov O, Chen CH, Mondal T, Deen MJ. Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring. IEEE Rev Biomed Eng 2018; 11:306-321. [PMID: 29993585 DOI: 10.1109/rbme.2018.2840336] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Electrocardiography (ECG) is the most common and extensively used vital sign monitoring method in modern healthcare systems. Different designs of ambulatory ECG systems were developed as alternatives to the commonly used 12-lead clinical ECG systems. These designs primarily focus on portability and user convenience, while maintaining signal integrity and lowering power consumption. Here, a wireless ECG monitoring system is developed using flexible and dry capacitive electrodes for long-term monitoring of cardiovascular health. Our capacitive-coupled dry electrodes can measure ECG signals over a textile-based interface material between the skin and electrodes. The electrodes are connected to a data acquisition system that receives the raw ECG signals from the electrodes and transmits the data using Bluetooth to a computer. A software application was developed to process, store, and display the ECG signal in real time. ECG measurements were obtained over different types of textile materials and in the presence of body movements. Our experimental results show that the performance of our ECG system is comparable to other reported ECG monitoring systems. In addition, to put this research into perspective, recent ambulatory ECG monitoring systems, ECG systems-on-chip, commercial ECG monitoring systems, and different state-of-the-art ECG systems are reviewed, compared, and critically discussed.
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Chou HH, Tsai CY, Jiang JS. An Experimental Study of a Micro-Projection Enabled Optical Terminal for Short-Range Bidirectional Multi-Wavelength Visible Light Communications. SENSORS 2018; 18:s18040983. [PMID: 29587457 PMCID: PMC5948546 DOI: 10.3390/s18040983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 03/19/2018] [Accepted: 03/23/2018] [Indexed: 12/02/2022]
Abstract
A micro-projection enabled short-range communication (SRC) approach using red-, green- and blue-based light-emitting diodes (RGB-LEDs) has experimentally demonstrated recently that micro-projection and high-speed data transmission can be performed simultaneously. In this research, a reconfigurable design of a polarization modulated image system based on the use of a Liquid Crystal on Silicon based Spatial Light Modulator (LCoS-based SLM) serving as a portable optical terminal capable of micro-projection and bidirectional multi-wavelength communications is proposed and experimentally demonstrated. For the proof of concept, the system performance was evaluated through a bidirectional communication link at a transmission distance over 0.65 m. In order to make the proposed communication system architecture compatible with the data modulation format of future possible wireless communication system, baseband modulation scheme, i.e., Non-Return-to-Zero On-Off-Keying (NRZ_OOK), M-ary Phase Shift Keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) were used to investigate the system transmission performance. The experimental results shown that an acceptable BER (satisfying the limitation of Forward Error Correction, FEC standard) and crosstalk can all be achieved in the bidirectional multi-wavelength communication scenario.
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Affiliation(s)
- Hsi-Hsir Chou
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
| | - Cheng-Yu Tsai
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
| | - Jhih-Shan Jiang
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
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Wei TY, Chang DW, Liu YD, Liu CW, Young CP, Liang SF, Shaw FZ. Portable wireless neurofeedback system of EEG alpha rhythm enhances memory. Biomed Eng Online 2017; 16:128. [PMID: 29132359 PMCID: PMC5684759 DOI: 10.1186/s12938-017-0418-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 11/02/2017] [Indexed: 11/26/2022] Open
Abstract
Background Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group. Methods The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8–12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively. Results The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8–12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group. Conclusions Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.
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Affiliation(s)
- Ting-Ying Wei
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Da-Wei Chang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - You-De Liu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chen-Wei Liu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Ping Young
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Fu Liang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.,Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
| | - Fu-Zen Shaw
- Department of Psychology, National Cheng Kung University, Tainan, Taiwan. .,Mind Research and Imaging Center, National Cheng Kung University, Tainan, Taiwan.
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15
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False alarm reduction in BSN-based cardiac monitoring using signal quality and activity type information. SENSORS 2015; 15:3952-74. [PMID: 25671512 PMCID: PMC4367394 DOI: 10.3390/s150203952] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 01/30/2015] [Indexed: 01/14/2023]
Abstract
False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring.
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