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Electrical Engineering and Systems Science > Signal Processing

arXiv:2305.05105 (eess)
[Submitted on 9 May 2023 (v1), last revised 26 Aug 2023 (this version, v3)]

Title:TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection

Authors:Zhenge Jia, Dawei Li, Cong Liu, Liqi Liao, Xiaowei Xu, Lichuan Ping, Yiyu Shi
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Abstract:The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and development competition. TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/machine learning (AI/ML) algorithms on implantable devices. The challenge problem of TDC'22 is to develop a novel AI/ML-based real-time detection algorithm for life-threatening ventricular arrhythmia over low-power microcontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs). The dataset contains more than 38,000 5-second intracardiac electrograms (IEGMs) segments over 8 different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC'22, which is open to multi-person teams world-wide, attracted more than 150 teams from over 50 organizations. This paper first presents the medical problem, dataset, and evaluation procedure in detail. It further demonstrates and discusses the designs developed by the leading teams as well as representative results. This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.
Comments: The paper is about the first TinyML design contest for healthcare
Subjects: Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2305.05105 [eess.SP]
  (or arXiv:2305.05105v3 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2305.05105

Submission history

From: Zhenge Jia [view email]
[v1] Tue, 9 May 2023 00:24:04 UTC (4,858 KB)
[v2] Tue, 23 May 2023 22:31:15 UTC (4,858 KB)
[v3] Sat, 26 Aug 2023 14:45:33 UTC (3,700 KB)
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