Abstract
Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.
Highlights
The electrocardiogram (ECG) is one of the major physiological signals generated by the heart
The annotated ECG records from the MIT-BIH arrhythmia database consists of 48 records, which are used for QRS detection in this study [41]
The proposed algorithm was tested on the ECG signals taken from the first channel of the MIT-BIH arrhythmia database
Summary
The electrocardiogram (ECG) is one of the major physiological signals generated by the heart. The QRS complex reflects the combination of three graphical deflections seen on a typical electrocardiogram that marks the depolarization of the ventricles It is usually the central and most visually obvious part of the ECG trace because of the larger size of ventricles. The Hamilton–Tompkins algorithm proposed by Tompkins in 1986 is one of the most typical QRS detection algorithms and is widely used because of its high accuracy and capability in real-time detection [13] Later, this algorithm has been improved by adding an automatic adjustment of the primary threshold in order to avoid subjective selection of threshold coefficients, which is called as Tompkins Modified Method II [11]. In this paper, based on detailed analysis of beat features in ECG, we proposed a simple and novel method with high average accuracy to detect QRS in ECGs with atypically shaped QRS complexes and noises.
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