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Mobile GPU-based implementation of automatic analysis method for long-term ECG.

Xiaomao Fan1,2,3,4, Qihang Yao1,3,4, Ye Li1,3,4

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Biomedical Engineering Online
|May 5, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a parallel algorithm for analyzing long-term electrocardiogram (ECG) data on mobile devices, significantly reducing processing time and battery consumption. This innovation enhances user experience and enables efficient remote cardiac monitoring.

Keywords:
Automatic ECG analysisEnergy consumptionMobile GPUParallel computing

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Area of Science:

  • Computational Medicine
  • Biomedical Signal Processing
  • Mobile Health Technology

Background:

  • Long-term electrocardiogram (ECG) monitoring is crucial for diagnosing intermittent cardiac arrhythmias.
  • Wearable holters and healthcare platforms facilitate remote cardiac monitoring, but face computational challenges.
  • Processing extensive ECG data on mobile devices is hindered by limited processing power.

Purpose of the Study:

  • To propose a novel parallel algorithm for automatic ECG analysis.
  • To leverage mobile graphics processing unit (GPU) for reduced ECG data processing time.
  • To optimize the use of heterogeneous mobile computing resources (CPU and GPU).

Main Methods:

  • Developed a parallel automatic ECG analysis algorithm by parallelizing time-consuming segments of a sequential algorithm.
  • Reorganized the ECG analysis pipeline to utilize both mobile CPU and GPU resources.
  • Evaluated the algorithm on a clinical long-term ECG dataset.

Main Results:

  • Achieved an average speedup of 5.81× in processing time for long-term ECG data (23.0 ± 1.0 h duration).
  • Reduced average algorithm execution time to 1.215 ± 0.140 s without compromising accuracy.
  • Decreased battery energy consumption by 64.16% (79.44% excluding data loading).

Conclusions:

  • The developed algorithm significantly reduces response time and energy consumption for mobile ECG analysis.
  • Enhanced user experience for holter device users.
  • Enables mobile devices to serve as efficient ECG terminals for healthcare professionals for onsite patient ECG inspection.