ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM

TLDR

Signal processing techniques are commonly used for real‑time ECG analysis, but classical methods struggle with the signal’s nonstationary nature. The paper proposes a DOST–DCT based time‑frequency representation of ECG signals. The method extracts DOST–DCT time‑frequency features, reduces them with PCA, augments them with RR‑interval dynamics, and classifies the resulting feature set with an SVM whose hyperparameters are optimized by particle swarm optimization, evaluated on 16 arrhythmia classes from the MIT‑BIH database. The approach achieved 98.82% overall accuracy, sensitivity, and positive predictivity, outperforming existing methods.

Abstract

Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the nonstationary nature of the ECG signal. In this context, this paper presents a new approach, i.e., discrete orthogonal stockwell transform using discrete cosine transform for efficient representation of the ECG signal in time-frequency space. These time-frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using support vector machine (SVM). In order to improve the classification performance, particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this paper, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. The experimental results yielded an improved overall accuracy, sensitivity (Sp), and positive predictivity (Pp) of 98.82% in comparison with the existing approaches available in the literature.