ECG Diagnostics based on the Filter-Bank Signal Processing and ANN / SVM Classification

Processing and classification of electrocardiogram (ECG) recordings are some of the most challenging fields of biomedical signal processing owing to the fact that ECG signals commonly exhibit complex temporal morphology and contain numerous artifacts of data collection process. This paper presents a comparative analysis between the Artificial Neural Networks and Support Vector Machines classification performances based on the feature vectors developed from the Filter-Bank processing of ECG signal. The system is evaluated in the context of Supraventricular Arrhythmia diagnostics. FIR Filter-Bank decomposes the ECG waveform into a various frequency components and enables independent temporal and spectral processing of ECG signal. The feature vectors are developed as a set of statistical measures that describe the energy distribution in the individual sub-bands. The considered statistical descriptors include mean, variance, skewness and kurtosis. In this paper, a systematic study of diagnostic performance is imposed on the choice of feature vector. An optimal Filter-Bank size is ascertained and the relevance of individual frequency bands is evaluated. Furthermore, the diagnostic relevance of statistical descriptors is assessed. The experimental results demonstrate that optimization of feature vectors, in terms of sub-band selection and statistical descriptor choices, leads to a considerable reduction in the feature vector size and to an improvement in the classification accuracy rate. Key-Words: Biomedical signal processing; ECG diagnostics; filter-banks; support vector machines; ANN