Key result
A deep neural network model with residual blocks (DNN-RB) classified cardiac cycles into six ECG beat classes with a test accuracy of 99.51%, average sensitivity of 99.7%, and specificity of 98.2%.
Why the study?
Manual interpretation of ECG signals can be challenging and time-consuming even for experts, driving the need for automatic ECG classification methods.
A novel deep neural network model with residual blocks demonstrates high accuracy (99.51%) in classifying ECG beats from a single lead, potentially aiding automated clinical and out-of-hospital monitoring.
ML models with tuned hyperparameters achieve high ECG classification accuracy; leaves open prospective clinical validation before practice adoption.
The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.
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Issa et al. (2023) studied Cardiovascular disease. Deep neural network model with residual blocks (DNN-RB) vs. Other state-of-the-art algorithms was evaluated on Classification of cardiac cycles into six ECG beat classes. A deep neural network model with residual blocks (DNN-RB) classified cardiac cycles into six ECG beat classes with a test accuracy of 99.51%, average sensitivity of 99.7%, and specificity of 98.2%.
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