Why the study?
Heart attacks cause approximately 10 million deaths annually, but various current limitations hinder ECG-based heart disease diagnosis.
Does the BiDLNet deep learning model improve the diagnostic accuracy of heart disease from ECGs compared to individual machine learning classifiers?
Does the BiDLNet deep learning model improve the diagnostic accuracy of heart disease from ECGs compared to individual machine learning classifiers?
The BiDLNet deep learning model demonstrates high accuracy in classifying normal and abnormal ECGs, suggesting potential utility as an automated diagnostic tool for heart disease.
Every year, around 10 million people die due to heart attacks. The use of electrocardiograms (ECGs) is a vital part of diagnosing these conditions. These signals are used to collect information about the heart's rhythm. Currently, various limitations prevent the diagnosis of heart diseases. The BiDLNet model is proposed in this paper which aims to examine the capability of electrocardiogram data to diagnose heart disease. Through a combination of deep learning techniques and structural design, BiDLNet can extract two levels of features from the data. A discrete wavelet transform is a process that takes advantage of the features extracted from higher layers and then adds them to lower layers. An ensemble classification scheme is then made to combine the predictions of various deep learning models. The BiDLNet system can classify features of different types of heart disease using two classes of classification: binary and multiclass. It performed remarkably well in achieving an accuracy of 97.5% and 91.5%, respectively.
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Kusuma et al. (2022) studied this question.
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