GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

doi:10.1109/EMBC.2016.7591930

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm

Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug:2016:5327-5330. doi: 10.1109/EMBC.2016.7591930.

Authors

Tae Joon Jun, Hyun Ji Park, Hyuk Yoo, Young-Hak Kim, Daeyoung Kim

PMID: 28269463
DOI: 10.1109/EMBC.2016.7591930

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.

Publication types

Evaluation Study

MeSH terms

Algorithms
Arrhythmias, Cardiac / diagnosis*
Cloud Computing*
Electrocardiography / instrumentation
Electrocardiography / methods*
Humans
Signal Processing, Computer-Assisted
Smartphone