A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

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.

Review of noise removal techniques in ECG signals

“…Fig. 1 a shows various components of ECG signal which are of two types, viz., morphological features: P‐wave, QRS‐complex, T‐wave, and U‐wave and interval features: PR‐segment, ST‐segment, PR interval, ST interval, RR interval, and so on [3–5 ]. ECG signals have a wide variety of applications in the medical domain such as cardiorespiratory monitoring, seizure detection and monitoring, ECG‐based biometrics authentication, real‐time analysis of electrocardiographic rhythm, heart‐rate variability analysis using smart electrocardiography patch, and study of cardiac ischemia [6–11 ].…”

Development of measuring system for determining life-threatening cardiac arrhythmias in a patient’s free activity

“…Japanese scientists in their ECG devices to suppress noise used the median filtering algorithm (median filter), for filtering impulse noise, smoothing signals, and isolating low-frequency noise [10]. The central count in the sliding window is replaced by the median (the average position report in the ranked row), removing the anomalous counts regardless of their amplitude values.…”

ECG Beat Classification using a Sliding Window and Correlation of the Three-bit Linear Prediction Error Signal

“…During recording, the ECG signal is usually disrupted by one or more kinds of noise or artefacts, such as: power-line interference, baseline drift, electrode motion artefact, data-collecting device noise and electromyogram (EMG) noise due to motion artefacts and muscle contraction [14], [15]. EMG noise is the factors that most severely affects the value of the ECG signal.…”