TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
This addresses the challenge of improving health monitoring for patients with cardiac conditions by enabling more efficient arrhythmia detection on implantable devices, though it is incremental as it builds on existing contest frameworks and focuses on a specific domain.
The paper describes the first TinyML Design Contest (TDC'22), which tackled the problem of developing AI/ML algorithms for real-time detection of life-threatening ventricular arrhythmia on low-power implantable devices, using a dataset of over 38,000 IEGM segments and attracting over 150 teams from 50+ organizations.
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.