Key result
Wavelet scattering transform algorithms classify ECG heart diseases with ~99.9% accuracy.
Why the study?
Due to increasing complexity and nonlinearity, direct observation and analysis of ECG signals is difficult.
Does a wavelet scattering transform-based machine learning method accurately classify ECG signals into arrhythmia, congestive heart failure, and normal sinus rhythm?
Does a wavelet scattering transform-based machine learning method accurately classify ECG signals into arrhythmia, congestive heart failure, and normal sinus rhythm?
A novel wavelet scattering transform-based machine learning approach demonstrates exceptionally high accuracy (>99%) in classifying ECG signals for arrhythmia and heart failure.
High-accuracy wavelet scattering ECG classification is feasible; leaves open prospective validation before clinical adoption.
The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.
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Heyam A. Marzog (2022) studied Arrhythmia, congestive heart failure, and normal sinus rhythm. Wavelet scattering transform-based method with Neural Network (NN) and Support Vector Machine (SVM) was evaluated on Classification accuracy. A wavelet scattering transform-based method achieved high classification accuracy for ECG heart diseases, reaching 99.7% with a neural network and 99.92% with a support vector machine.
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