Ajoy K. Ray

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 Symlet-2 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.

The Application of Genetic Algorithm for Unsupervised Classification of ECG

In this chapter,we have proposed an integrated methodology for electrocardiogram (ECG) based differentiation of arrhythmia and normal sinus rhythm using genetic algorithm optimized k-means clustering. Open source databases consisting of the MIT BIH arrhythmia and MIT BIH normal sinus rhythm data are used. The methodology consists of QRS-complex detection using the Pan-Tompkins algorithm, principal component analysis (PCA), and subsequent pattern classification using the k-means classifier, error back propagation neural network (EBPNN) classifier, and genetic algorithm optimized k-means clustering. The m-fold cross-validation scheme is used in choosing the training and testing sets for classification. The k-means classifier provides an average accuracy of 91.21 % over all folds, whereas EBPNN provides a greater average accuracy of 95.79 %. In the proposed method, the k-means classifier is optimized using the genetic algorithm (GA), and the accuracy of this classifier is 95.79 %, which is equal to that of EBPNN. In conclusion, the classification accuracy of simple unsupervised classifiers can be increased to near that of supervised classifiers by optimization using GA. The application of GA to other unsupervised algorithms to yield higher accuracy as a future direction is also observed.