A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

The World Health Organization (WHO) sheds light on the escalating prevalence of heart diseases, foreseeing a substantial rise in the years ahead, impacting a vast global population. Swift and accurate early detection becomes pivotal in managing severe complications, underscoring the urgency of timely identification. While Ventricular Ectopic Beats (V) might initially be considered normal, their frequent occurrence could serve as a potential red flag for progressing to severe conditions like atrial fibrillation, Ventricular Tachycardia, and even cardiac arrest. This accentuates the need for developing an automated approach for early detection of cardiovascular diseases (CVD). This paper presents a novel method to classify arrhythmias. Leveraging the Wavelet Scattering Transform (WST) to extract morphological features from Electrocardiogram heartbeats (ECG), these features seamlessly integrate into a 1D Convolutional Neural Network (CNN). The CNN is finely tuned to distinguish between V, Supraventricular Ectopic Beats (S), and Non-Ectopic Beats (N). Our model's performance surpasses state-of-the-art models, boasting precision, sensitivity, and specificity of 94.56%, 97.26%, and 99.54% for V, and 99.25%, 98.65%, and 93.26% for N. Remarkably, it achieves 68.01% precision, 77.75% sensitivity, and 99.14% specificity for S.

@article{Bourkha2024,
  title     = {A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {2},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Elmehdi Ait Bourkha and Anas Hatim and Dounia Nasir and Said El Beid and Assia Sayed Tahiri},
  doi       = {10.14569/IJACSA.2024.0150265},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150265}
}