Abstract
Abnormalities in the heart’s electrical signal can lead to irregularities in the heart normal rhythm or can entirely collapse the functioning of the heart. An electrocardiogram (ECG) measures the heart’s electrical activity and contains many features that are indicative of such irregularities. This paper presents a novel strategy for detecting the irregularities in the heart rhythm from the recorded ECG signal using a combination of software and hardware tools. MWCNT/PDMS composite based dry electrodes are used to record the ECG signal for both static and with some physical activities. The least mean square (LMS) algorithm based adaptive filtering is implemented to acquire motion-free, clean, and reliable ECG data. The adaptive filtering exhibits the signal-to-noise ratio (SNR) as 42 dB, which demonstrates the efficiency of the filtering by ~8% compared to the raw ECG data. Once a clean ECG signal is acquired, it is sent for further processing and features of interest are extracted in LabVIEW that could be used to detect any abnormalities in the ECG signal. Overall, the proposed strategy helps in identifying heart rhythm irregularities quickly, accurately, and efficiently.
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