Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction
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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/9884076 |
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| author | Heyam A. Marzog Haider. J. Abd |
| author_facet | Heyam A. Marzog Haider. J. Abd |
| author_sort | Heyam A. Marzog |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0668d820ebd643f5812abc135986b1e5 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-0668d820ebd643f5812abc135986b1e52025-02-03T06:00:55ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/9884076Machine Learning ECG Classification Using Wavelet Scattering of Feature ExtractionHeyam A. Marzog0Haider. J. Abd1Electrical Engineering DepartmentElectrical Engineering DepartmentThe 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.http://dx.doi.org/10.1155/2022/9884076 |
| spellingShingle | Heyam A. Marzog Haider. J. Abd Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction Applied Computational Intelligence and Soft Computing |
| title | Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction |
| title_full | Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction |
| title_fullStr | Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction |
| title_full_unstemmed | Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction |
| title_short | Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction |
| title_sort | machine learning ecg classification using wavelet scattering of feature extraction |
| url | http://dx.doi.org/10.1155/2022/9884076 |
| work_keys_str_mv | AT heyamamarzog machinelearningecgclassificationusingwaveletscatteringoffeatureextraction AT haiderjabd machinelearningecgclassificationusingwaveletscatteringoffeatureextraction |