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.
Highlights
According to the World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally [1]
ECGs are used in the initiation of therapy in patients with acute coronary syndromes and the diagnosis of intraventricular conduction disturbances and arrhythmias [3]
ECG recordings may be collected with different durations, requiring a robust and numerically-efficient algorithm to analyze the long-recorded data and detect their dynamic and characteristic waves [4]
Summary
According to the World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally [1]. A variety of programs and policies have been implemented in increasingly diverse communities to provide tools, strategies, and other best practices to reduce the incidence of initial and recurrent cardiovascular events [2]. To achieve this goal, the electrocardiogram (ECG) has become the most commonly used biosignal for the prompt detection of CVDs. ECGs are used in the initiation of therapy in patients with acute coronary syndromes and the diagnosis of intraventricular conduction disturbances and arrhythmias [3]. T wave morphology may vary because of altered ventricular activation [5]
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