Comparison of several classifiers to evaluate endocardial electrograms fractionation in human

Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. A novel algorithm for automated description of atrial electrograms (A-EGMs) fractionation based on wavelet transform and several statistical pattern recognition methods was proposed and new methodology of A-EGM processing was designed and tested. The algorithms for A-EGM classification were developed using normal density based classifiers, linear and high degree polynomial classifiers, nearest mean scaled classifiers, nonlinear classifiers, neural networks and j48. All classifiers were compared and tested using a representative set of 1.5 s A-EGMs (n = 68) ranked by 3 independent experts 100% coincidentialy into 4 classes of fractionation: 1 - organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation. Feature extraction and well performing classification algorithms tested here showed maximal error of 15% and mean classification error across all implemented classifiers 9%, and the best mean classification error 5.9% (nearest mean classifier), and classification error of highly fractionated A-EGMs of approximately 9%.