An efficient approach for Paroxysmal Atrial Fibrillation events prediction using Extreme Learning Machine

Article type: Research Article

Authors: Maghawry, Eman^{; *} | Ismail, Rasha | Gharib, Tarek F.

Affiliations: Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

Correspondence: [*] Corresponding author. Eman Maghawry, Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt. E-mail: [email protected].

Abstract: Paroxysmal Atrial Fibrillation (PAF) is a special class of Atrial Fibrillation. Predicting PAF events from electrocardiogram (ECG) signal streams plays a vital role in generating real-time alerts for cardiac disorders. These alerts are extremely important to cardiologists in taking precautions to prevent their patients from having a stroke. In this study, an effective predictive approach to PAF events using the Extreme Learning Machine classification technique is proposed. Besides, we propose a feature extraction method that integrates new ECG signal features to its time-domain ones. The new features are based on the construction of sparse vectors for peaks in ECG signals that provide high overlap between similar ECGs. The proposed prediction approach with the new ECG features representation were evaluated on a real PAF dataset using the five-fold cross-validation method. Experiments show promising results for predicting PAF in terms of accuracy and execution time compared to other existing studies. The proposed approach achieved classification accuracy of 97% for non-streaming ECG signals mode and 94.4% for streaming mode.

Keywords: Paroxysmal atrial fibrillation, feature extraction, extreme learning machine, electrocardiogram (ECG) signals classification, streaming ECG Signals

DOI: 10.3233/JIFS-201832

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5087-5099, 2021