Fig. 9
From: Hybrid CNN-BLSTM architecture for classification and detection of arrhythmia in ECG signals

Comparative performance evaluation of the CNN and CNN-BLSTM models across five arrhythmia classes (N, R, L, V, A) using (a) Precision, (b) Recall, (c) F1-score, and (d) ROC-AUC metrics. The CNN-BLSTM consistently outperforms the baseline CNN across all metrics, demonstrating superior classification accuracy and robustness.