Key result
Application of the GPU-based Segmented-Beat Modulation Method significantly increased the signal-to-noise ratio of athlete electrocardiograms during training from 1±5 dB to 19±5 dB (p<10^-12).
Why the study?
Training electrocardiograms can help identify underlying cardiovascular diseases causing sport-related sudden cardiac death, but fast, accurate processing is challenged by high noise and interferences.
Does the GPU-based Segmented-Beat Modulation Method (GPU-SBMM) improve signal quality and processing speed for denoising athlete electrocardiograms during training?
Does the GPU-based Segmented-Beat Modulation Method (GPU-SBMM) improve signal quality and processing speed for denoising athlete electrocardiograms during training?
GPU-SBMM provides a fast and effective method for denoising exercise-induced artifacts in athlete electrocardiograms, potentially aiding in real-time monitoring to prevent sport-related sudden cardiac death.
May support real-time ECG monitoring in athletes; hypothesis-generating, with prospective trials needed before clinical adoption.
Sport-related sudden cardiac death (SRSCD), defined as "death occurring during sport or within one hour of cessation of training", is the leading cause of death in athletes. SRSCD occurs in the presence of underlying cardiovascular diseases, some of which may be identified by processing electrocardiographic recordings acquired during training (TECGs). A fast and accurate processing of TECGs during or immediately after training is challenging since TECGs are typically highly corrupted by noise and interferences, which may jeopardize their interpretation and identification of abnormal morphologies. The present study evaluated the ability of GPU-based Segmented-Beat Modulation Method (GPU-SBMM) to provide a noise-free estimation of TECGs, and to improve the algorithm by GPU acceleration to make it compatible with modern hardware. In this research, 19 6to-10 min TECGs (sampling frequency: 256 Hz), acquired from 8 subjects while performing 4 different exercise tasks (walk, run, low-resistance bike and high-resistance bike), were analyzed. Results indicate that GPU-SBMM application yielded a significant increase of SNR(dB) (from 15 dB to 195 dB; p<10 -12 ), also when stratifying by exercise tasks. Additionally, a considerable average speedup of 7.67x is achieved using NVIDIA GeForce 740M GPU processor.
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Nasim et al. (2018) studied Exercise-induced electrocardiogram noise (n=8). GPU-based Segmented-Beat Modulation Method (GPU-SBMM) vs. Unprocessed noisy ECG (for SNR) and CPU-based SBMM (for speed) was evaluated on Signal-to-noise ratio (SNR) in decibels (p=<10^-12). Application of the GPU-based Segmented-Beat Modulation Method significantly increased the signal-to-noise ratio of athlete electrocardiograms during training from 1±5 dB to 19±5 dB (p<10^-12).
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