research-article

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

Authors:

Mathews M. John,

Joseph Cavallaro,

Behnaam Aazhang,

Yingyan LinAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 18, Issue 2

Article No.: 29, Pages 1 - 25

https://doi.org/10.1145/3465372

Published: 16 March 2022 Publication History

Abstract

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.

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Cited By

Si THe FLi P(2024)UnifiedSC: a unified framework via collaborative optimization for multi-task person re-identificationApplied Intelligence10.1007/s10489-024-05333-054:4(2962-2975)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1007/s10489-024-05333-0
Jia ZChen JXu XKheir JHu JXiao HPeng SHu XChen DShi Y(2023)The importance of resource awareness in artificial intelligence for healthcareNature Machine Intelligence10.1038/s42256-023-00670-05:7(687-698)Online publication date: 12-Jun-2023
https://doi.org/10.1038/s42256-023-00670-0

Index Terms

RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Information & Contributors

Information

Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 18, Issue 2

April 2022

411 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3508462

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 16 March 2022

Accepted: 01 May 2021

Revised: 01 April 2021

Received: 01 December 2020

Published in JETC Volume 18, Issue 2

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Cited By

Si THe FLi P(2024)UnifiedSC: a unified framework via collaborative optimization for multi-task person re-identificationApplied Intelligence10.1007/s10489-024-05333-054:4(2962-2975)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1007/s10489-024-05333-0
Jia ZChen JXu XKheir JHu JXiao HPeng SHu XChen DShi Y(2023)The importance of resource awareness in artificial intelligence for healthcareNature Machine Intelligence10.1038/s42256-023-00670-05:7(687-698)Online publication date: 12-Jun-2023
https://doi.org/10.1038/s42256-023-00670-0

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