Key result
A GPU-based cloud system using a parallelized K-NN algorithm achieved a 93.5% arrhythmia detection rate and was 2.5 times faster than a CPU-only algorithm.
Why the study?
Does a GPU based Cloud system with parallel K-NN algorithm improve execution time for arrhythmia detection compared to a CPU only algorithm?
Does a GPU based Cloud system with parallel K-NN algorithm improve execution time for arrhythmia detection compared to a CPU only algorithm?
Effect estimate: 2.5 times faster execution time
A GPU-based cloud system using a parallelized K-NN algorithm significantly accelerates arrhythmia detection execution time while maintaining a high detection rate.
May accelerate cloud-based arrhythmia detection; leaves open prospective clinical validation and outcome impact.
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.
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Jun et al. (2016) studied Arrhythmia. GPU based Cloud system with parallelized K-NN algorithm vs. CPU only detection algorithm was evaluated on Arrhythmia detection rate and execution time (2.5 times faster execution time). A GPU-based cloud system using a parallelized K-NN algorithm achieved a 93.5% arrhythmia detection rate and was 2.5 times faster than a CPU-only algorithm.
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