Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
Siti Nurmaini, Alexander Edo Tondas, Annisa Darmawahyuni, Muhammad Naufal Rachmatullah, Radiyati Umi Partan, Firdaus Firdaus, Bambang Tutuko, Ferlita Pratiwi, Andre Herviant Juliano, Rahmi Khoirani
Abstract
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low-cost method for identifying this condition is the use of a single-lead electrocardiogram (ECG) as the gold standard for AF diagnosis, after annotation by experts. However, manual interpretation of these signals may be subjective and susceptible to inter-observer variabilities because many non-AF rhythms exhibit irregular RR-intervals and lack P-waves similar to AF. Furthermore, the acquired surface ECG signal is always contaminated by noise. Hence, highly accurate and robust detection of AF using short-term, single-lead ECG is valuable but challenging. To improve the existing model, this paper proposes a simple algorithm of a discrete wavelet transform (DWT) coupled with one-dimensional convolutional neural networks (1D-CNNs) to classify three classes: Normal Sinus Rhythm (NSR), AF and non-AF (NAF). The experiment was conducted with a combination of three public datasets and one dataset from an Indonesian hospital. The robustness of the proposed model was evaluated based on several validation data with an unseen pattern from 4 datasets. The results indicated that 1D-CNNs outperformed other approaches and achieved satisfactory performances with high generalization ability. The accuracy, sensitivity, specificity, precision, and F1-Score for two classes were 99.98%, 99.91%, 99.91%, 99.99%, and 99.95%, respectively. For the three classes, the accuracy, sensitivity, specificity, precision, and F1-Score was 99.17%, 98.90%, 99.17%, 96.74%, and 97.48%, respectively. Potentially, our approach can aid AF diagnosis in clinics and patient self-monitoring to improve early detection and effective treatment of AF.