A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia

- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in the automated prediction of heart disease. A deep bi-directional capsule network (Bi-CapsNet) uses a new method based on an intelligent deep learning (DL) classifier model to make the classification process very accurate. Initially, the input ECG signal data are acquired and the preprocessing steps such as DC drift, normalization, LPF filtering, spectrogram analysis, and artifact removal are applied. After preprocessing the data, the Deep Ensemble CNN-RNN approach is employed for feature extraction. Finally, the Deep Bi-CapsNet model is used to predict and classify the cardiac arrhythmia. For performance validation, the dataset is referred to the MIT-BIH arrhythmia database, which selects five different types of arrhythmias from the ECG waveform to estimate the proposed model. Various ECG arrhythmia categories, including Normal (NOR), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB), and Left Bundle Branch Block (LBBB) have been identified. For performance analysis, the metrics such as precision, accuracy, F1-score, error rate, sensitivity, false positive rate, specificity, Mathew coefficient, Kappa coefficient, and outcomes are included and compared with the traditional methods to validate the effectiveness of the implemented scheme. The proposed scheme has achieved an overall accuracy rate of approximately 97.19 % compared to the traditional deep learning models like CNN (89.87 %), FTBO (85 %), and Capsule Network (97.0 %). The comparison results indicate that the proposed hybrid model outperforms these traditional models.