Key result
A proposed 1D CNN model using a Random Forest classifier achieved a diagnostic accuracy of 98.3% in classifying four types of ECG records.
Why the study?
Early detection of heart diseases using ECG signals requires robust diagnostic methods to accurately classify different ECG abnormalities.
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A proposed 1D CNN model combined with a Random Forest classifier achieved 98.3% accuracy in diagnosing four types of ECG records, demonstrating potential for automated early detection of heart diseases.
One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.
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Bassiouni et al. (2022) studied Heart diseases (ECG diagnosis) (n=294). 1D CNN model with Random Forest classifier vs. 1D CNN model with Softmax classifier was evaluated on Diagnostic accuracy. A proposed 1D CNN model using a Random Forest classifier achieved a diagnostic accuracy of 98.3% in classifying four types of ECG records.
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