Feature extraction based on time-frequency and Independent Component Analysis for improvement of separation ability in Atrial Fibrillation detector.

Due to redundancy of over-dimensioned information, observed often in originally recorded biomedical signals, feature extraction and selection has become focus of much researches connected with biomedical signal processing and classification. Mixed new feature vector combined from time-frequency signal representation (obtained after wavelet transform) and Independent Component Analysis (ICA) applied for non-stationary signals is proposed as a preliminary stage in ECG waveform classification for patients with Atrial Fibrillation (AF). Discrete fast wavelet transform coefficients parameters including energy and entropy measures and components extracted as a result of FastICA algorithm implementation after optimization gave the best classifier performance of whole AF ECG classifier system. System was positively verified on the set of clinically classified ECG signals for control and atrial fibrillation (AF) disease patients taken from MITBIH data base. The measures of specificity and sensitivity computed for the set of 20 AF and 20 patients from control group divided into learning and verifying subsets were used to evaluate presented pattern recognition structure. Different types of wavelet basic functions for feature extraction stage and kernels for SVM classifier structure calculation were tested to find the best system architecture. Obtained results showed, that the ability of generalization and separation for enriched feature extraction based system increased, due to selectively choosing only the most representative features for analyzed AF detection problem.