Key result
An optimized artificial neural network using wavelet transform and the invasive weed optimization algorithm achieved a total classification accuracy of over 80% and sensitivity of over 70% for classifying eight types of ECG beats.
Why the study?
Real-time implementation of automated QRS detection algorithms in low-cost systems remains challenging due to limited hardware resources and processing delays.
An optimized neural network using wavelet transform and invasive weed optimization can effectively classify ECG signals into 8 categories with high accuracy, potentially reducing computational complexity for real-time applications.
Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.
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Jawad et al. (2022) studied Cardiac abnormalities (ECG signal classification). Wavelet transform with optimized neural network (IWO algorithm) was evaluated on Total classification accuracy (TCA). An optimized artificial neural network using wavelet transform and the invasive weed optimization algorithm achieved a total classification accuracy of over 80% and sensitivity of over 70% for classifying eight types of ECG beats.
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