AAMI Based ECG Heart-Beat Time-Series Clustering Using Unsupervised ELM and Decision Rule

Abstract

Early detection of cardiovascular diseases can prevent the premature deaths caused by abnormal heartbeat problems. Application of unsupervised classification by Extreme learning machine is addressed for ElectroCardiogram (ECG) heart-beat time series clustering by a hybrid of Extreme learning machine and Decision rule using full heart-beat time series by alignment of R-peaks of all beats is proposed in this work. 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. Because of the large patient specific characteristics in ECG heartbeat morphology across individuals, the supervised methods trained on some ECG dataset may decrease performance on other datasets. In addition, these techniques require a considerable amount of known and labelled heartbeats which are not feasible in long–term ECG monitoring. 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 98.13%, 82.25%, 76.49% and 52.20%, PPV of 98.13%, 64.46%, 95.47%, 46.54% for N, S, V, and F classes respectively and TCA of 95.75%.