Key result
A proposed hybrid feature extraction method with a back propagation neural network achieved 95.07% accuracy for ECG classification, compared to 97.64% for K-nearest neighbor.
Why the study?
Accurate ECG classification for diagnosing heart diseases remains a challenging problem despite various existing methods.
Does a hybrid feature extraction method combined with machine learning algorithms improve the classification accuracy of ECG signals for heart disease?
Does a hybrid feature extraction method combined with machine learning algorithms improve the classification accuracy of ECG signals for heart disease?
Absolute Event Rate: 95.07% vs 97.64%
A hybrid feature extraction method combined with machine learning algorithms achieves high accuracy (>95%) in classifying ECG signals for heart disease diagnoses.
May aid automated ECG interpretation; leaves open whether hybrid methods outperform KNN in clinical practice.
Electrocardiogram (ECG) records electrical activities of human heart that can help identify abnormalities. ECG signals are widely used to classify the signals into various categories that help medical specialists in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. Different methods have been used and studied for classification of cardiac signals and classification of heart rhythm disorders in recent years. Temporal, morphological, and statistical features are commonly used for the classification of ECG signals. In this paper, we propose a hybrid feature extraction method by applying correlation and regression methods in addition to the three techniques mentioned above. Moreover, we propose Back propagation neural network (BPNN) to classify heart disorders. Furthermore, the results were compared with other traditional machine learning algorithms, including K-nearest neighbor (KNN), Decision Tree (DT) and Random Forest (RF). The obtained results demonstrate that the proposed feature extraction combination technique positively affects the BPNN model and applied traditional machine learning techniques. Proposed BPNN achieved 95.07% accuracy, DT 96.09%, KNN 97.64% and RF 96.74%.
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Akcin et al. (2021) studied Heart disease. Hybrid feature extraction method and Back propagation neural network (BPNN) vs. Traditional machine learning algorithms (KNN, DT, RF) was evaluated on Classification accuracy. A proposed hybrid feature extraction method with a back propagation neural network achieved 95.07% accuracy for ECG classification, compared to 97.64% for K-nearest neighbor.
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