RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms
RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms
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DOI：
10.1145/3465372
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发表时间：
2021-11
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通讯作者：
Yongan Zhang;Anton Banta;Yonggan Fu;M. John;A. Post;M. Razavi;Joseph R. Cavallaro;B. Aazhang;Yingyan Lin
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作者：
Yongan Zhang;Anton Banta;Yonggan Fu;M. John;A. Post;M. Razavi;Joseph R. Cavallaro;B. Aazhang;Yingyan Lin
起搏器提供的信号(即心内电(EGM))和医生用来诊断异常心律的信号(即12导联心电图(ECG))之间存在差距。因此，前者即使远程传播，也不足以让医生提供准确的诊断，更不用说及时进行干预了。为了缩小这一差距，并在对不规则和罕见的室性心律的即时反应进行实时关键干预方面迈出了启发式的一步，我们提出了一种名为RT-RCG的新框架，该框架可以自动搜索(1)有效的深度神经网络(DNN)结构，然后(2)相应的加速器，从而能够实时、高质量地从EGM信号中重建心电信号。具体地说，RT-RCG提出了一种新的DNN搜索空间，该空间专为从EGM信号重建心电而定制，并结合了可微加速搜索(DAS)引擎，以高效地在大型离散加速器设计空间中导航，以生成优化的加速器。广泛的实验和各种环境下的消融研究一致地验证了我们的RT-RCG的有效性。据我们所知，RT-RCG是第一个利用神经结构搜索(NAS)同时处理重建效率和效率的方法。
There 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.