Toward Automated Analysis of Electrocardiogram Big Data by Graphics Processing Unit for Mobile Health Application
With the rapid development of mobile health technologies and applications in recent years, large amounts of electrocardiogram (ECG) signals that need to be processed timely have been produced. Although the CPU-based sequential automated ECG analysis algorithm (CPU-AECG) designed for identifying seve...
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| Veröffentlicht in: | IEEE access 2017-01, Vol.5, p.17136-17148 |
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| creator | Fan, Xiaomao Chen, Runge He, Chenguang Cai, Yunpeng Wang, Pu Li, Ye |
| description | With the rapid development of mobile health technologies and applications in recent years, large amounts of electrocardiogram (ECG) signals that need to be processed timely have been produced. Although the CPU-based sequential automated ECG analysis algorithm (CPU-AECG) designed for identifying seven types of heartbeats has been in use for years, it is single-threaded and handling lots of concurrent ECG signals still poses a severe challenge. In this paper, we propose a novel GPU-based automated ECG analysis algorithm (GPU-AECG) to effectively shorten the program executing time. A new concurrencybased GPU-AECG, named cGPU-AECG, is also developed to handle multiple concurrent signals. Compared with the CPU-AECG, our cGPU-AECG achieves a 35 times speedup when handling 24-h-long ECG data, without reducing the classification accuracy. With cGPU-AECG, we can handle 24-h-ECG signals from thousands of users in a few seconds and provide prompt feedback, which not only greatly improves the user experience of mobile health services, but also reduces the economic cost of building healthcare platforms. |
| doi_str_mv | 10.1109/ACCESS.2017.2743525 |
| format | Article |
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(IEEE) 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-739c7dafb6a135afe3b2fec54d402d4f8ca391d136188f07ad2ef9d6d014bc763</citedby><cites>FETCH-LOGICAL-c408t-739c7dafb6a135afe3b2fec54d402d4f8ca391d136188f07ad2ef9d6d014bc763</cites><orcidid>0000-0002-5351-8546</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8016339$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27610,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Fan, Xiaomao</creatorcontrib><creatorcontrib>Chen, Runge</creatorcontrib><creatorcontrib>He, Chenguang</creatorcontrib><creatorcontrib>Cai, Yunpeng</creatorcontrib><creatorcontrib>Wang, Pu</creatorcontrib><creatorcontrib>Li, Ye</creatorcontrib><title>Toward Automated Analysis of Electrocardiogram Big Data by Graphics Processing Unit for Mobile Health Application</title><title>IEEE access</title><addtitle>Access</addtitle><description>With the rapid development of mobile health technologies and applications in recent years, large amounts of electrocardiogram (ECG) signals that need to be processed timely have been produced. 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Although the CPU-based sequential automated ECG analysis algorithm (CPU-AECG) designed for identifying seven types of heartbeats has been in use for years, it is single-threaded and handling lots of concurrent ECG signals still poses a severe challenge. In this paper, we propose a novel GPU-based automated ECG analysis algorithm (GPU-AECG) to effectively shorten the program executing time. A new concurrencybased GPU-AECG, named cGPU-AECG, is also developed to handle multiple concurrent signals. Compared with the CPU-AECG, our cGPU-AECG achieves a 35 times speedup when handling 24-h-long ECG data, without reducing the classification accuracy. 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| subjects | Algorithm design and analysis Algorithms automated ECG analysis Automation Central processing units Classification algorithms concurrent computing CPUs Economic impact Electrocardiography Feature extraction GPU computing Graphics processing units Heart beat Medical services mobile health parallel algorithm Pipelines Signal processing |
| title | Toward Automated Analysis of Electrocardiogram Big Data by Graphics Processing Unit for Mobile Health Application |
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