TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection

TL;DR

This paper discusses a challenge to develop low-power, real-time AI/ML algorithms for detecting life-threatening ventricular arrhythmia in implantable devices, highlighting innovative solutions and their results from a global competition.

Contribution

It introduces a novel medical detection challenge using TinyML on microcontrollers, providing a dataset, evaluation methods, and analysis of top solutions.

Findings

Top solutions achieved real-time detection with low power consumption

The dataset includes over 38,000 intracardiac electrogram segments

The challenge attracted over 150 teams worldwide

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.