Recognition of Dangerous Rhythm Disturbances from Short ECG Fragments

Recognition methods of dangerous rhythm disturbances using the spectral description of short electrocardiosignals are the subject of this paper. Each object is described by 15 spectral features in the range from 0 to 15 Hz, all data are normalized to total signal strength. The sample size is 270 objects of each class. The method of Fisher linear discriminant (FLD) is used, as well as the method of convex hulls classification. The results of the binary classification of arrhythmias into non-dangerous and dangerous using two methods are presented. The performances of the classification efficiency (sensitivity, specificity, and overall accuracy) are calculated. Leave-one-out cross-validation (LOO CV) is used during the classification experiment to empirically evaluated the generalization ability of the algorithm. Also, mapping of two classes' objects onto the plane of vectors, obtained using the FLD method is presented. The convex hull classification based on linear programming has an overall accuracy of 91.5%. The results of this work can be used to solve practical problems in medicine.