•  
  •  
 

Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

KAJEETH KUMAR GURUSAMY: 0009-0000-7549-586X

MUTHURAJKUMAR SANNASY: 0000-0003-3960-6926

Abstract

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features critical for detecting various types of arrhythmias. The method is evaluated using the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) Arrhythmia Database. The experimental results demonstrate that the proposed method achieves competitive performance compared with real-valued CNNs and existing approaches for arrhythmia detection under the same evaluation protocol. Specifically, the proposed method obtained an accuracy of 99.74%, precision of 99.36%, recall of 99.30%, specificity of 99.84%, and F1-score of 99.36%. A 10-fold cross-validation scheme is employed to evaluate the model’s effectiveness, yielding superior performance relative to conventional methods. Overall, the proposed approach constitutes a competitive complex-valued learning framework for ECG-based arrhythmia classification under beat-level evaluation and offers a robust tool for artificial intelligence (AI)-based medical diagnostics.

DOI

10.55730/1300-0632.4181

Keywords

Complex-valued neural network, arrhythmia detection, electrocardiogram signal classification, Hilbert transform, artificial intelligence, time-frequency analysis

First Page

385

Last Page

400

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Download

Share

COinS