GDL

Journal from gdlhub / 2021-08-24 12:07:31
Oleh : Vishwanath Bhagyalakshmi, Ramchandra Vittal Pujeri, Geetha Dundesh Devanagavi, King Saud University
Dibuat : 2021-08-24, dengan 0 file

Keyword : Arrhythmia classification, Genetic algorithm, Bat optimization, ECG signals, ECG wave intervals, SVNN
Url : http://www.sciencedirect.com/science/article/pii/S1319157817302823
Sumber pengambilan dokumen : Web

Arrhythmia is a cardiac condition generated by the abnormal electrical activity of the heart, and an electrocardiogram (ECG) is a tool utilized by the cardiologists for determining the arrhythmias or heart abnormalities. Owing to the existence of noise, the non-stationary nature of the ECG signal and the abnormality of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. Hence, there is a need for computer-aided diagnosis system which can achieve higher recognition accuracy. Since the existing methods of classification consumed huge time and less accurate in case of considerably similar ECG signal, this paper proposes an effective method termed as Genetic Bat optimization algorithm for training the Support Vector Neural Network (GB-SVNN) for arrhythmia classification using ECG signals. Initially, multi-resolution wavelet-based approach and the Gabor filters are used for extracting the wave interval features and other texture features from the ECG signal. Based on the features, the SVNN classifies the ECG signal as the affected arrhythmia or no arrhythmia signal. The SVNN is trained using the GB algorithm. The experimentation of the proposed method is done using MATLAB 2015.a, and the performance is evaluated with the existing methods, such as KNN, Neural Network (NN), Fuzzy Subtractive Clustering, and Support Vector Neural Network (SVNN) for accuracy, specificity, and sensitivity. From the results, it can be shown that the proposed GD-SVNN attains a maximum value of accuracy, sensitivity, and specificity at a rate of 0.9696, 0.99, and 0.9583 respectively.

Deskripsi Alternatif :

Arrhythmia is a cardiac condition generated by the abnormal electrical activity of the heart, and an electrocardiogram (ECG) is a tool utilized by the cardiologists for determining the arrhythmias or heart abnormalities. Owing to the existence of noise, the non-stationary nature of the ECG signal and the abnormality of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. Hence, there is a need for computer-aided diagnosis system which can achieve higher recognition accuracy. Since the existing methods of classification consumed huge time and less accurate in case of considerably similar ECG signal, this paper proposes an effective method termed as Genetic Bat optimization algorithm for training the Support Vector Neural Network (GB-SVNN) for arrhythmia classification using ECG signals. Initially, multi-resolution wavelet-based approach and the Gabor filters are used for extracting the wave interval features and other texture features from the ECG signal. Based on the features, the SVNN classifies the ECG signal as the affected arrhythmia or no arrhythmia signal. The SVNN is trained using the GB algorithm. The experimentation of the proposed method is done using MATLAB 2015.a, and the performance is evaluated with the existing methods, such as KNN, Neural Network (NN), Fuzzy Subtractive Clustering, and Support Vector Neural Network (SVNN) for accuracy, specificity, and sensitivity. From the results, it can be shown that the proposed GD-SVNN attains a maximum value of accuracy, sensitivity, and specificity at a rate of 0.9696, 0.99, and 0.9583 respectively.

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