A Novel Method of QRS Detection using Time and Amplitude Thresholds with Statistical False Peak Elimination

The electrocardiogram (ECG) captures cardiac activity through the QRS complex, which is centered at the R-peak of each heartbeat. However, accurate analysis of ECG signals is often impeded by low-frequency baseline wander, high-frequency noise, interference from P and T waves, and variations in QRS morphology. Among the various tasks in ECG analysis, QRS complex detection is crucial for effective heart monitoring and diagnosis. ECG signal processing primarily involves three key aspects: signal denoising, wave detection, and heartbeat classification. Notably, signal denoising plays a vital role in enhancing the accuracy of subsequent analyses by mitigating noise artifacts. This study proposes a novel peak detection algorithm that effectively suppresses noise while adapting to variations in ECG morphology, thereby improving detection performance. The proposed algorithm is based on wavelet transformation and is evaluated against the Statistical False Peak Elimination (SFPE) method, incorporating median and moving average filters. Given the non-stationary nature of ECG signals, wavelet-based techniques prove to be highly effective for signal denoising, wave detection, and heartbeat classification, demonstrating superior performance in ECG analysis.