Enhanced Atrial Fibrillation Detection-based Wavelet Scattering Transform with Time Window Selection and Neural Network Integration

Atrial Fibrillation (AF), a prevalent anomaly in cardiac rhythm, significantly impacts a substantial portion of the population, with projections indicating an escalation in its prevalence in the near future. This disorder manifests as irregular and accelerated heartbeats originating within the heart's upper chambers known as the atria. Neglecting to address this condition could potentially lead to serious consequences, particularly an elevated susceptibility to stroke and heart failure. This underscores the critical importance of developing an automated approach for detecting AF. In our study, an automatic approach was introduced for classifying short single-lead Electrocardiogram (ECG) recordings signals into four categories: Atrial fibrillation (AF), Normal rhythm (N), Noisy rhythm (~), or Other rhythms (O). The wavelet scattering network (WSN) is employed to extract morphological features from the ECG signals, which are then inputted into an Artificial Neural Network (ANN) with time windows selection and majority vote. The results from the testing data exhibit that our proposed model outperforms the state-of-art models, achieving a remarkable overall accuracy of 87.35% and an F1 score of 89.13%.

@article{Bourkha2023,
  title     = {Enhanced Atrial Fibrillation Detection-based Wavelet Scattering Transform with Time Window Selection and Neural Network Integration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Elmehdi Ait Bourkha and Anas Hatim and Dounia Nasir and Said El Beid},
  doi       = {10.14569/IJACSA.2023.0141252},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141252}
}