RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms

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dc.citation.articleNumber29en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleACM Journal on Emerging Technologies in Computing Systemsen_US
dc.citation.volumeNumber18en_US
dc.contributor.authorZhang, Yonganen_US
dc.contributor.authorBanta, Antonen_US
dc.contributor.authorFu, Yongganen_US
dc.contributor.authorJohn, Mathews M.en_US
dc.contributor.authorPost, Allisonen_US
dc.contributor.authorRazavi, Mehdien_US
dc.contributor.authorCavallaro, Josephen_US
dc.contributor.authorAazhang, Behnaamen_US
dc.contributor.authorLin, Yingyanen_US
dc.date.accessioned2022-06-08T17:02:33Zen_US
dc.date.available2022-06-08T17:02:33Zen_US
dc.date.issued2022en_US
dc.description.abstractThere exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable Real-Time and high-quality Reconstruction of ECG signals from EGM signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.en_US
dc.identifier.citationZhang, Yongan, Banta, Anton, Fu, Yonggan, et al.. "RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms." <i>ACM Journal on Emerging Technologies in Computing Systems,</i> 18, no. 2 (2022) ACM: https://doi.org/10.1145/3465372.en_US
dc.identifier.doihttps://doi.org/10.1145/3465372en_US
dc.identifier.urihttps://hdl.handle.net/1911/112458en_US
dc.language.isoengen_US
dc.publisherACMen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by ACM.en_US
dc.titleRT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrogramsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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