T. Torfs, R. Yazicioglu, Sunyoung Kim, Hyejung Kim, C. van Hoof, Dilpreet Buxi, I. Romero, J. Wijsman, F. Massé, J. Penders
{"title":"Ultra low power wireless ECG system with beat detection and real time impedance measurement","authors":"T. Torfs, R. Yazicioglu, Sunyoung Kim, Hyejung Kim, C. van Hoof, Dilpreet Buxi, I. Romero, J. Wijsman, F. Massé, J. Penders","doi":"10.1109/BIOCAS.2010.5709564","DOIUrl":null,"url":null,"abstract":"A wireless ECG monitoring system is presented that is able to perform high-quality ECG signal acquisition, beat detection, and real time monitoring of skin-electrode impedance which can be used to monitor the presence of motion artefacts. The whole system consumes only 170μW while performing local beat detection. The beat detection algorithm was verified against the MIT-BIH arrhythmia database and obtains a median sensitivity of 99.74% and positive predictivity of 99.87%. The system was validated using applied signals at varying signal to noise ratio as well as on human volunteers.","PeriodicalId":440499,"journal":{"name":"2010 Biomedical Circuits and Systems Conference (BioCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Biomedical Circuits and Systems Conference (BioCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOCAS.2010.5709564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
A wireless ECG monitoring system is presented that is able to perform high-quality ECG signal acquisition, beat detection, and real time monitoring of skin-electrode impedance which can be used to monitor the presence of motion artefacts. The whole system consumes only 170μW while performing local beat detection. The beat detection algorithm was verified against the MIT-BIH arrhythmia database and obtains a median sensitivity of 99.74% and positive predictivity of 99.87%. The system was validated using applied signals at varying signal to noise ratio as well as on human volunteers.
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