Wavelet-based Machine Learning Techniques for ECG Signal Analysis

Machine learning of ECG is a core component in any of the ECG-based healthcare informatics system. Since the ECG is a nonlinear signal, the subtle changes in its amplitude and duration are not well manifested in time and frequency domains. Therefore, in this chapter, we introduce a machine-learning approach to screen arrhythmia from normal sinus rhythm from the ECG. The methodology consists of R-point detection using the Pan-Tompkins algorithm, discrete wavelet transform (DWT) decomposition, sub-band principal component analysis (PCA), statistical validation of features, and subsequent pattern classification. The k-fold cross validation is used in order to reduce the bias in choosing training and testing sets for classification. The average accuracy of classification is used as a benchmark for comparison. Different classifiers used are Gaussian mixture model (GMM), error back propagation neural network (EBPNN), and support vector machine (SVM). The DWT basis functions used are Daubechies-4, Daubechies-6, Daubechies-8, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, and Coiflet-5. An attempt is made to exploit the energy compaction in the wavelet sub-bands to yield higher classification accuracy. Results indicate that the Symlet2 wavelet basis function provides the highest accuracy in classification. Among the classifiers, SVM yields the highest classification accuracy, whereas EBPNN yields a higher accuracy than GMM. The use of other time frequency representations using different time frequency kernels as a future direction is also observed. The developed machine-learning approach can be used in a web-based telemedicine system, which can be used in remote monitoring of patients in many healthcare informatics systems. R. J. Martis (&) C. Chakraborty School of Medical Science and Technology, IIT, Kharagpur, India e-mail: [email protected] A. K. Ray Department of Electronics and Electrical Communication Engineering, IIT, Kharagpur, India S. Dua et al. (eds.), Machine Learning in Healthcare Informatics, Intelligent Systems Reference Library 56, DOI: 10.1007/978-3-642-40017-9_2, Springer-Verlag Berlin Heidelberg 2014 25