ACAC: Automatic Cardiac Arrhythmia Classification Based on 1D-Deep Resnet

Arrhythmias are infrequent irregularities in a person's heartbeat rhythm. They result from any disruption in the ordered pattern of the heart's expanding wave of excitation, a process heavily reliant on electrical activity. These arrhythmias might result in consequences that could be fatal and constitute a direct risk to life. As a result, detecting and classifying arrhythmias is a significant challenge in cardiac diagnostics. Electrocardiograms (ECG) are widely used in the diagnosis of arrhythmias because they are a low-cost, non-invasive, and rapid technique of diagnosis. However, the unpredictability of arrhythmic segments, along with the sensitivity of ECGs to noise, leads to arrhythmia misdiagnosis. Furthermore, manually identifying cardiac arrhythmias using ECG data takes time and is liable for inaccuracy. Deep learning (DL) is a preferred approach for fast and automatic ECG signal classification, surpassing the performance of traditional machine learning models. The suggested study proposes a novel deep learning architecture, namely t-dimensional deep residual neural network (1D-deep ResNet) for an Automatic cardiac arrhythmia classification (ACAC) system. ECG signals from the MIT -BIH dataset were used to train and evaluate the model. This proposed model contributes in two different ways. First, the unbalanced ECG data are corrected using the upsampling approach to reduce noise and prevent biased prediction outcomes. Next, the unbalanced ECG signals are automatically classified using 1D-Deep ResNet. ACAC system aims to overcome the issues of traditional electrocardiograms (ECGs) in the identification of arrhythmias, which can be impacted by noise and unpredictability of events, leading to misinterpretation and mistakes. The confusion matrix is used to calculate the model accuracy, AUC, precision, recall, and f1 score to evaluate the model performance. The results of the experiments shows that the proposed method performed well, with 99.9% and 99.94% AUC score in the training and testing datasets, respectively. The proposed model outperforms other existing deep learning approaches like CNN, LSTM, and GRU in terms of performance, and it will significantly minimise the involvement of doctors in the classification of ECG signals. According to our research findings, the 1D-deep ResNet is more suitable for automated cardiac arrhythmia classification in the future than other deep learning algorithms now available.