An ECG classification system using wavelet transform, autoregressive modelling, and support vector machines achieved an overall accuracy of 99.68% for recognizing 6 heart rhythm types.
A proposed machine learning approach using wavelet transform, autoregressive modeling, and SVM achieves 99.68% accuracy in classifying 6 types of ECG heart rhythms in computer simulations.
This paper presents a new approach to the feature extraction for reliable heart rhythm recognition. This system of classffication is comprised of three components including data preprocessing, feature extraction and classification of ECG signals. Two different feature extraction methods are applied together to obtain the feature vector of ECG data. The wavelet transform is used to extract the coefficients of the transform as the features of each ECG segment. Simultaneously, autoregressive modelling(AR) is also applied to obtain the temporal structures of ECG waveforms. Then the support vector machine(SVM) with Gaussian kernel is used to classify different ECG heart rhythm. Computer simulations are provided to verify the performance of the proposed method. From computer simulations, the overall accuracy of classffication for recognition of 6 heart rhythm types reaches 99.68%.
Zhao et al. (2006) studied Heart rhythm recognition. Wavelet transform, autoregressive modelling, and support vector machines was evaluated on Overall accuracy of classification for recognition of 6 heart rhythm types. An ECG classification system using wavelet transform, autoregressive modelling, and support vector machines achieved an overall accuracy of 99.68% for recognizing 6 heart rhythm types.