Inter-patient heart-beat classification using complete ECG beat time series by alignment of R-peaks using SVM and decision rule

An ElectroCardiogram (ECG) inter-patient heartbeat time series classification method by a hierarchical system of based on support vector machine and Decision rule, using full heart-beat time series by alignment of R-peaks of all beats, is proposed. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Experiments were conducted on ECG data of 44 patients obtained from MIT-BIH Arrhythmia database. Results were compared with existing methods such as weighted support vector machine (SVM), hierarchical SVM and weighted linear discriminant analysis (LDA). Comparative analysis confirms the viability and superiority of the proposed approach in terms of Total classification accuracy (TCA). Proposed system achieved Sensitivities of 98.7%, 85.9%, 88.8%, 58.3%, PPV% of 98.53%, 82.2%, 89.9%, 85.6% for N, S, V and F classes respectively and a TCA of 97.3%.