Model Based Interpretation for ECG signal analysis

ECG signal analysis is very much needed for clinical diagnosis. This book describes various signal processing techniques that can be used for ECG analysis and arrhythmia detection. Five signal processing algorithms aimed at enhancement of the ECG data and subsequent arrhythmia detection are (1) Multiscale principal component analysis (MSPCA) based algorithm for enhancing the ECG data, (2) Cumulant based autoregressive modeling algorithm for ECG enhancement, (3) Higher order statistics (HOS) for arrhythmia detection, (4) Cumulant based Teager energy operator(TEO) for arrhythmia detection, (5) PVC identification using Discrete cosine transform (DCT)-Teager energy operator (TEO) model. Various statistical measures are used for performance analysis of the proposed methods. Required data for testing these algorithms is taken from Physionet Archive.