Why the study?
Heart abnormalities are difficult to detect visually due to subtle changes in ECG signals, requiring computer-aided diagnosis to reduce fatality among cardiac patients.
Does an automatic ECG diagnosis system using DWT, HOS, and entropy-based feature selection improve arrhythmia classification accuracy compared to other methods?
Does an automatic ECG diagnosis system using DWT, HOS, and entropy-based feature selection improve arrhythmia classification accuracy compared to other methods?
A novel machine learning approach combining DWT, HOS, and entropy-based feature selection achieves >99% accuracy in classifying ECG arrhythmias.
Proposed ECG arrhythmia CAD method requires validation; leaves open clinical utility pending prospective trials.
Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.
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Chashmi et al. (2019) studied this question.
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