Literature DB >> 25561446

Low-power wireless ECG acquisition and classification system for body sensor networks.

Shuenn-Yuh Lee, Jia-Hua Hong, Cheng-Han Hsieh, Ming-Chun Liang, Shih-Yu Chang Chien, Kuang-Hao Lin.   

Abstract

A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on-off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc-air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.

Mesh:

Year:  2015        PMID: 25561446     DOI: 10.1109/JBHI.2014.2310354

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  7 in total

1.  Robust cardiac event change detection method for long-term healthcare monitoring applications.

Authors:  Udit Satija; Barathram Ramkumar; M Sabarimalai Manikandan
Journal:  Healthc Technol Lett       Date:  2016-05-13

2.  Flexible quality of service model for wireless body area sensor networks.

Authors:  Yangzhe Liao; Mark S Leeson; Matthew D Higgins
Journal:  Healthc Technol Lett       Date:  2016-03-16

Review 3.  From Pacemaker to Wearable: Techniques for ECG Detection Systems.

Authors:  Ashish Kumar; Rama Komaragiri; Manjeet Kumar
Journal:  J Med Syst       Date:  2018-01-11       Impact factor: 4.460

4.  Evaluation of an Automated Swallow-Detection Algorithm Using Visual Biofeedback in Healthy Adults and Head and Neck Cancer Survivors.

Authors:  Gabriela Constantinescu; Kristina Kuffel; Daniel Aalto; William Hodgetts; Jana Rieger
Journal:  Dysphagia       Date:  2017-11-02       Impact factor: 3.438

5.  Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer.

Authors:  Sadik Kamel Gharghan; Saif Saad Fakhrulddin; Ali Al-Naji; Javaan Chahl
Journal:  Sensors (Basel)       Date:  2019-10-14       Impact factor: 3.576

6.  A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network.

Authors:  Yuan-Ho Chen; Szi-Wen Chen; Pei-Jung Chang; Hsin-Tung Hua; Shinn-Yn Lin; Rou-Shayn Chen
Journal:  Sensors (Basel)       Date:  2022-01-21       Impact factor: 3.576

7.  Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly.

Authors:  Luis J Mena; Vanessa G Félix; Alberto Ochoa; Rodolfo Ostos; Eduardo González; Javier Aspuru; Pablo Velarde; Gladys E Maestre
Journal:  Comput Math Methods Med       Date:  2018-05-29       Impact factor: 2.238

  7 in total

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