Robust Heartbeat Detector Based on Weighted Correlation and Multichannel Input: Implementation on the ECG Recorded with Textile Electrodes

Linda Rattfält, Maria Lindén, Peter Hult, Per Ask and Magnus Borga
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Linda Rattfält: Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Maria Lindén: Intelligent Sensor Systems Department, Mälardalen University, Västerås, Sweden
Peter Hult: Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Per Ask: Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Magnus Borga: Department of Biomedical Engineering, Linköping University, Linköping, Sweden

International Journal of E-Health and Medical Communications (IJEHMC), 2013, vol. 4, issue 1, 61-71

Abstract: The aim of this study was to develop and evaluate a robust heartbeat detector for noisy electrocardiograms (ECGs) recorded with textile electrodes. The authors suggest a method based on weighted correlation in a multi-channel ECG to obtain a heartbeat detector. Signals were acquired during rest and at movements which simulate every day activities. From each recording a segment corresponding to a heartbeat was extracted and correlated with the whole signal. From the correlation data, heartbeat candidates were derived and weighted based on their variance similarity with the heartbeat model and previous heartbeats. Finally, the outputs of each channel were added to create the global output. The output was compared to the Pan Tompkins heartbeat detector. Results are promising for recordings at rest (sensitivity = 0.97, positive predictive value (PPV) = 0.97). For static muscle tension in the torso the results were much higher than the reference method (sensitivity = 0.77, PPV = 0.85). Corresponding values for the reference method were sensitivity = 0.96 and PPV = 0.95 at rest and sensitivity = 0.52 and PPV = 0.75 during muscle tension.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jehmc0:v:4:y:2013:i:1:p:61-71

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