Five well-known arrhythmia classification algorithms were compared in this paper based on the recommendations in AAMI standard. They are C4.5, k-Nearest Neighbor, Multilayer Perceptron, PART, and Support Vector Machine, respectively, with inputs related to heartbeat intervals and ECG morphological features. They were evaluated on three independent datasets, including the MIT-BIH arrhythmia database, a collection of ECG signals acquired from healthy subjects by the wireless Body Sensor Network (BSN) nodes, and a third dataset captured also by the BSN nodes. Results showed the overall accuracy on the MIT-BIH arrhythmia database was approximately 99.04%, with high sensitivity, specificity, and selectivity. When tested with ECG signals acquired from the human subjects, which were partially deteriorated due to several factors, e.g., motion artifacts and data transmission problems, the overall accuracy of 94.19% and that of 81.22% were obtained for static activities and dynamic activities, respectively. In addition, the effects of the signal quality from these human subjects on false alarms were investigated. When false alarms occurring in signal segments with low quality were excluded, the number of false detections reduced from 14.17% to 8.65%. When evaluated on signals generated by the patient simulator, which included several types of premature ventricular contraction without artifacts from body movements, a high classification accuracy was also observed.
Keywords: Arrhythmia classification; ambulatory monitoring; body sensor network; signal quality; wireless ECG.