Delineating QRS detector parameter based ECG-Beat classification

Abstract

Abstract The electrocardiogram is a very valuable clinical tool which allows to retrieve information about the presence and location of arrhythmic foci as well as ischemic and scar tissue and disorder’s of the dedicated cardiac conduction system. In the presented study timing parameters computed by a delineating beat detector for identifying the P-Wave, QRS - complex and T-Wave are used to classify the individual beats. From a set of total 419 feature generated from these parameters 64 are used to train LDA classifier for discriminating 3 classes (Normal, Artifact, Arrhythmic) and 5 Classes (Normal, Artifact, Atrial and ventricular premature contractions and bundle branch blocks). Further it is investigated how the imbalance between normal beats and arrhythmic beats as well as the beats missed by the beat detector affect the classification results. In the case of 5 classes accuracies of 97.52 % in the imbalanced case and 96.38 r for the balanced data were obtained. For 3 classes accuracies of 97.76 % and 95.18 % were achieved. Considering in addition the beats missed by the detector the accuracies dropped to 96.68 %, and 95.54 % for 5 classes and 95.54 % and 96.92 % for 3 classes. These values are within the ranges for linear classifier reported in literature. This is quite promising for implementing a real-time classifier which exploits the parameters and values computed by the beat detector.