Robust Heartbeat Detector Based on Weighted Correlation and Multichannel Input

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.

Physiological parameters monitoring of fire-fighters by means of a wearable wireless sensor system

“…On the other hand, our intention is to replace all cables, even the transmission lines carrying the RF signal, with conductive yarn routes on the firefighter suit [8][9][10][11]. Moreover, we intend to replace ECG sensors with textile electrodes, either based on conductive yarns or screen-printed [12,13], while respiration rate, temperature and skin moisture (perspiration) could be monitored using appropriately designed variations of conductive yarn based knitted sensors [14][15][16][17]. Finally, even the antenna could be partially or fully textile [18,19].…”