Key result
A multi-class kernel support vector machine approach with Bayesian optimization attained an ECG signal recognition rate of 99.25% for classifying arrhythmia, heart failure, and normal rhythms.
Why the study?
Does a multi-class kernel SVM with Bayesian optimization improve the recognition rate of ECG signals in patients with ARR, CHF, and NSR compared to other machine learning approaches?
Does a multi-class kernel SVM with Bayesian optimization improve the recognition rate of ECG signals in patients with ARR, CHF, and NSR compared to other machine learning approaches?
A multi-class kernel SVM approach with Bayesian optimization achieved a 99.25% recognition rate for classifying ECG signals of arrhythmia, heart failure, and normal sinus rhythm, with low memory requirement and fast response.
In this proposed work, an electrocardiogram (ECG) signal is classified using multi-class kernel function based support vector machine (SVM) approach with optmized hyperparameter using bayesian optmization (BO). Measured ECG signal is denoised using butterworth filter. Then the signal is converted into scalogram patterns and features are extracted using fractal image image compression technique for recognition. For validation, the propsoed algorithm comprised of three different input patterns like, people having cardiac arrhythmia (ARR), people having congestive heart failure (CHF), and people having normal sinus rhythms (NSR). The proposed algorithm attained the recognition rate of 99.25% compared to other machine learning approaches signifcant improvement is found. Additional beneifit of the proposed algorithm is required very less amount of memory with fast response.
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Kumar et al. (2022) studied Cardiac arrhythmia, congestive heart failure, and normal sinus rhythms. Multi-class kernel support vector machine (SVM) with Bayesian optimization vs. Other machine learning approaches was evaluated on Recognition rate. A multi-class kernel support vector machine approach with Bayesian optimization attained an ECG signal recognition rate of 99.25% for classifying arrhythmia, heart failure, and normal rhythms.
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