Why the study?
Early detection of heart diseases via ECG is important to reduce the rising worldwide death rate from heart disease.
Does a Convolution Neural Network (CNN) algorithm improve classification accuracy and computing efficiency for ECG signals compared to previous methods?
Does a Convolution Neural Network (CNN) algorithm improve classification accuracy and computing efficiency for ECG signals compared to previous methods?
A CNN algorithm improves the accuracy and computational efficiency of classifying ECG signals into five AAMI categories compared to existing methods.
It is widely known that heart disease is among the most significant cause of loss of life worldwide. Therefore, early detection of heart diseases is important to reduce the rising death rate. An electrocardiogram can detect many types of heart diseases including abnormal heart rhythms. We propose here a method for categorizing heart diseases entirely focused on ECG by using a machine learning method, known as Convolution Neural Networks (CNNs) into five categories as per the Association for Advancement of Medical Instrumentation (AAMI) standards. The outcomes conveys that the suggested methodology outperforms several previously available methods for ECG signal classification in respect of the classification accuracy and computing efficiency.
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Anis et al. (2022) studied this question.
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