Title: Fusion of multiple time-frequency representation techniques and classifiers for ECG and PPG signal analysis

Authors: Piyush Mahajan; Amit Kaul

Addresses: Electrical Engineering Department, National Institute of Technology Hamirpur (NITH), Anu, Hamirpur, 177005, Himachal Pradesh, India ' Electrical Engineering Department, National Institute of Technology Hamirpur (NITH), Anu, Hamirpur, 177005, Himachal Pradesh, India

Abstract: Time-frequency representation (TFR) techniques play a vital role in signal processing by revealing inherent characteristics of complex data. This study explores the fusion of 11 TFR techniques for analysing electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Signal analysis involves noise removal, characteristic point detection, and feature extraction, followed by classification. Models were trained to classify ECG signals into six arrhythmia classes and PPG signals into normal and abnormal (hypertension) categories. Several transforms achieved over 90% test accuracy, with ensemble classifiers performing exceptionally well, achieving up to 95.87% accuracy for ECG signals. Ensemble classifiers using continuous wavelet transform (CWT), S-transform, Wigner-Ville distribution (WVD), and synchrosqueezed wavelet transform (SSWT) demonstrated a 96.6% testing accuracy. Additionally, combining CWT and SSWT, the K-nearest neighbour classifier achieved 81.48% accuracy for PPG signals. The proposed approach fuses information from multiple TFR techniques, boosting ECG classification accuracy to 99.75% and PPG accuracy to 82.52%.

Keywords: electrocardiogram; ECG; photoplethysmogram; PPG; time-frequency representation; TFR; healthcare.

DOI: 10.1504/IJBET.2025.148102

International Journal of Biomedical Engineering and Technology, 2025 Vol.48 No.3, pp.251 - 286

Received: 26 Jul 2024
Accepted: 15 Dec 2024
Published online: 25 Aug 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article