Why the study?
Conventional ECG devices are often unable to detect intermittent cardiac arrhythmia symptoms during hospital visits.
Does the proposed wearable ECG processor accurately and efficiently detect cardiac arrhythmias?
Does the proposed wearable ECG processor accurately and efficiently detect cardiac arrhythmias?
The proposed energy-efficient wearable ECG processor achieves high accuracy (97.34%) in arrhythmia detection with very low power consumption, demonstrating potential for continuous ambulatory monitoring.
Wearable ECG processor enables intermittent arrhythmia detection; leaves open prospective clinical validation before practice adoption.
Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.
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Deng et al. (2023) studied this question.
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