Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets - Petmezas, Georgios; Haris, Kostas; Stefanopoulos, Leandros; Kilintzis, Vassilis; Tzavelis, Andreas; Rogers, John A; Katsaggelos, Aggelos K; Maglaveras, Nicos (Biomedical signal processing and control, 2021)
•The hybrid CNN-LSTM approach provides the best combination of performance (sensitivity, specificity) in comparison with all previous relevant studies.•The proposed model performs well for highly imbalanced datasets.•Focal loss function delivers better results than the classic cross-entropy function for ECG classification.•The proposed methodology could be used for real-time arrhythmia detection as the prediction phase lasts only a few seconds. Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related complications that can increase the risk of strokes and heart failure. Manual electrocardiogram (ECG) interpretation for its diagnosis is tedious, time-consuming, requires high expertise, and suffers from inter- and intra-observer variability. Deep learning techniques could be exploited in order for robust arrhythmia detection models to be designed. In this paper, we propose a novel hybrid neural model utilizing focal loss, an improved version of cross-entropy loss, to deal with training data imbalance. ECG features initially extracted via a Convolutional Neural Network (CNN) are input to a Long Short-Term Memory (LSTM) model for temporal dynamics memorization and thus, more accurate classification into the four ECG rhythm types, namely normal (N), atrial fibrillation (AFIB), atrial flutter (AFL) and AV junctional rhythm (J). The model was trained on the MIT-BIH Atrial Fibrillation Database and achieved a sensitivity of 97.87%, and specificity of 99.29% using a ten-fold cross-validation strategy. The proposed model can aid clinicians to detect common atrial fibrillation in real-time on routine screening ECG.