title: GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.
abstract: In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.
authors.lastname: Tae Joon Jun
authors.lastname: Hyun Ji Park
authors.lastname: Hyuk Yoo
authors.lastname: Young-Hak Kim
authors.lastname: Daeyoung Kim
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D000067917:Cloud Computing; D004562:Electrocardiography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D000068997:Smartphone