Abstract
A robust and numerically-efficient method based on two moving average filters, followed by a dynamic event-related threshold, has been developed to detect P and T waves in electrocardiogram (ECG) signals as a proof-of-concept. Detection of P and T waves is affected by the quality and abnormalities in ECG recordings; the proposed method can detect P and T waves simultaneously through a unique algorithm despite these challenges. The algorithm was tested on arrhythmic ECG signals extracted from the MIT-BIH arrhythmia database with 21,702 beats. These signals typically suffer from: (1) non-stationary effects; (2) low signal-to-noise ratio; (3) premature atrial complexes; (4) premature ventricular complexes; (5) left bundle branch blocks; and (6) right bundle branch blocks. Interestingly, our algorithm obtained a sensitivity of 98.05% and a positive predictivity of 97.11% for P waves, and a sensitivity of 99.86% and a positive predictivity of 99.65% for T waves. These results, combined with the simplicity of the method, demonstrate that an efficient and simple algorithm can suit portable, wearable, and battery-operated ECG devices.