Key result
Adaptive learning classifier improves heartbeat classification over prior methods without requiring expert labeling.
Why the study?
Automated detection of abnormal heartbeats from ECG signals is vital to provide timely intervention for patients with cardiovascular diseases and reduce morbidity and healthcare burden.
May reduce expert annotation needs in ECG diagnostics; leaves open prospective validation before clinical adoption.
Cardiovascular diseases (CVD) are a leading cause of unnecessary hospital admissions as well as fatalities placing an immense burden on the healthcare industry. A process to provide timely intervention can reduce the morbidity rate as well as control rising costs. Patients with cardiovascular diseases require quick intervention. Towards that end, automated detection of abnormal heartbeats captured by electronic cardiogram (ECG) signals is vital. While cardiologists can identify different heartbeat morphologies quite accurately among different patients, the manual evaluation is tedious and time consuming. In this chapter, we propose new features from the time and frequency domains and furthermore, feature normalization techniques to reduce inter-patient and intra-patient variations in heartbeat cycles. Our results using the adaptive learning based classifier emulate those reported in existing literature and in most cases deliver improved performance, while eliminating the need for labeling of signals by domain experts.
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Srinivas et al. (2015) studied Cardiovascular diseases (abnormal heartbeats). Adaptive learning based classifier with time and frequency domain features vs. Existing literature methods was evaluated on Heartbeat classification performance. An adaptive learning based classifier using new time and frequency domain features delivered improved heartbeat classification performance compared to existing literature without requiring expert labeling.
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