Deep Fusion of Temporal and Spectral Features in ECG for Automated Arrhythmia Classification

Abstract

The early identification of arrhythmia symptoms using electrocardiogram (ECG) signals is essential for successful treatment and avoidance of dreadful consequences, and this study enhances cardiovascular diagnostics by doing just that. Conventional electrocardiogram (ECG) methods of diagnosis often make inefficient and time-consuming use of time-frequency domain data. A new approach is suggested to address this limitation by exploring improved arrhythmia detection methods using Convolutional Neural Networks (CNNs). The goal of integrating the ethical domain is to develop a robust arrhythmia clinical system that can enhance the precision and efficacy of clinical practice. Voting Classifier (a mix of random forest and adaBoost) and stacking of classifies (a mix of random forest, MLP and Lightgbm) are among the response algorithms utilized throughout the version. Other algorithms include CNN, LSTM, CNN + LSTM, and Voting Classifier. An initial evaluation using the MIT-bit and PTDBD datasets demonstrates that the CNN model achieves an accuracy rate of 99.43%. Efforts are being made to further enhance accuracy by utilizing ensemble approaches in conjunction with CNN+LSTM, balloting Classifier, and Stacking Classifier. A huge leap forward in cardiovascular health is offered by these artworks diagnostics, with the goal of improving patient outcomes by optimizing the use of electrocardiogram (ECG) signal information through the application of deep learning and ensemble methods.