Mobile GPU-based implementation of automatic analysis method for long-term ECG.
Background: Long-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computa...
| Published in: | BioMedical Engineering OnLine Vol. 17; no. 1; pp. 56 - 57 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
BioMed Central
5/3/2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129434328&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129434328 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 5/3/2018 vid: 17 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 129434328 129434328 NLM29724227 10.1186/s12938-018-0487-3 NLM29724227 129434328 ppf: 56 ppct: 1 formats: tig: atl: Mobile GPU-based implementation of automatic analysis method for long-term ECG. aug: au: Fan, Xiaomao Yao, Qihang Li, Ye Chen, Runge Cai, Yunpeng affil: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China sug: subj: Electrocardiography Algorithms Signal Processing, Computer Assisted Computer Graphics Automation Ferrans and Powers Quality of Life Index Scales ab: Background: Long-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computational burden created by concurrent processing of numerous holter data poses a serious challenge to the healthcare platform. An alternative solution is to shift the analysis tasks from healthcare platforms to the mobile computing devices. However, long-term ECG data processing is quite time consuming due to the limited computation power of the mobile central unit processor (CPU).Methods: This paper aimed to propose a novel parallel automatic ECG analysis algorithm which exploited the mobile graphics processing unit (GPU) to reduce the response time for processing long-term ECG data. By studying the architecture of the sequential automatic ECG analysis algorithm, we parallelized the time-consuming parts and reorganized the entire pipeline in the parallel algorithm to fully utilize the heterogeneous computing resources of CPU and GPU.Results: The experimental results showed that the average executing time of the proposed algorithm on a clinical long-term ECG dataset (duration 23.0 ± 1.0 h per signal) is 1.215 ± 0.140 s, which achieved an average speedup of 5.81 ± 0.39× without compromising analysis accuracy, comparing with the sequential algorithm. Meanwhile, the battery energy consumption of the automatic ECG analysis algorithm was reduced by 64.16%. Excluding energy consumption from data loading, 79.44% of the energy consumption could be saved, which alleviated the problem of limited battery working hours for mobile devices.Conclusion: The reduction of response time and battery energy consumption in ECG analysis not only bring better quality of experience to holter users, but also make it possible to use mobile devices as ECG terminals for healthcare professions such as physicians and health advisers, enabling them to inspect patient ECG recordings onsite efficiently without the need of a high-quality wide-area network environment. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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