Hybrid deep learning model for heart disease detection on 12-lead electrocardiograms.

This research possesses the producing and taking the assessment of a new deep learning structure, namely, Deep Convolutional BiLSTM Hybrid Network, specialized for arrhythmia recognition before ECG records. Through PhysioNet Challenge 2021 dataset, which has collected different styles of the ECG recordings from nine different arrhythmia types, this study is trying to respond to the critical need that non-attendant and accurate arrhythmia detection is required in the medical field. By utilizing both convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) units that allow our model to carry out spatial and temporal operations, which is essential to the native dynamics existing ECG signal, we are able to get in view a more fine-grained analysis surpassing other traditional approaches. The suggested algorithm which has achieved accuracy, precision, recall, and F-score of 98.5%, 98.2%, 98.2%, and 98.4% percent respectively has proven to be a novel and more accurate solution compared to the existing state-of-the-art model when similar kinds of tasks are performed. These results not only reveal the effectiveness of CNNs and BiLSTM toginger in understanding complicated biomedical signs but also shed a light on the prospect of deep learning in raising the standards of dairgocis and health care practices. The research outcome of arrhythmia diagnosis by arranging new standard carpatients would bring a lot of changes to the existing medical informatics that provide a scalable solution that would be implemented to every healthcare institution globally for timely and accurate diagnosis. [ABSTRACT FROM AUTHOR]