Characterizing cardiac arrhythmia by optimized window length based PRSA technique

Phase Rectified Signal Averaging (PRSA) technique is a promising method for analysis of quasi-periodic signals which helps to identify characteristic frequencies contained in the data by disregarding the artifacts and noises. But the performance of the PRSA technique largely depends on the choice of window length (WL). It is required to optimize WL for better detection of precise but important periods present in signal. In this paper a method to optimize the window length is proposed based on the spectral analysis of original and PRSA signal. The proposed method is applied on ECG signal to characterize and classify the atrial fibrillation (AF), atrial flatter (AFL) and ventricular flutter (VFL) rhythms with statistical features. Classification of the fibrillatory episode is done by K-nearest-neighbor (KNN) and support vector machine (SVM) clustering method with derived features. Extracted features are clustered with a new approach of Root Mean Square (RMS) Technique. This algorithm is applied on to the MIT-BIH arrhythmia database and checks the performance. Both quantitative and qualitative analysis is made and sensitivity and specificity 98.24% and 96.08% respectively is achieved.