The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multimonth, 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 (VA) over low-power microcontrollers utilized in implantable cardioverter-defibrillators (ICDs). The dataset contains more than 38000 5-s intracardiac electrograms (IEGMs) segments over eight different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC’22, which is open to multiperson teams world-wide, attracted more than 150 teams from over 50 organizations. This article 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 article concludes with the direction of improvement for the future TinyML design for health monitoring applications.

Implantable Cardioverter Defibrillator (ICD) is an ultra-low-power device which monitors heart rate and delivers in-time defibrillation on detected ventricular arrhythmias (VAs). The parameters of VAs detection mechanism on each recipient’s ICD are supposed to be fine-tuned to obtain accurate detection due to the individual’s unique rhythm features. However, the process extremely relies on clinical expertise and thus must be conducted manually and routinely by cardiologists diagnosing massive amount of rhythm data. In this paper, we introduce a novel self-supervised on-device personalization of convolutional neural network (CNNs) for VAs detection. We first propose a computing framework consisting of an edge device and an ICD to enable efficient on-device CNNs personalization and real-time inference respectively. Then, we propose a generative model that learns to synthesize patient-specific intracardiac EGMs signals, which can then be used as personalized training data to improve patient-specific VAs detection performance on ICDs. Evaluations on three detection models show that the self-supervised on-device personalization significantly improve VAs detection performance under a patient-specific setting.

Life-threatening ventricular arrhythmias (VAs) detection on intracardiac electrograms (IEGMs) is essential to Implantable Cardioverter Defibrillators (ICDs). However, current VAs detection methods count on a variety of heuristic detection criteria, and require frequent manual interventions to personalize criteria parameters for each patient to achieve accurate detection. In this work, we propose a one-dimensional convolutional neural network (1D-CNN) based life-threatening VAs detection on IEGMs. The network architecture is elaborately designed to satisfy the extreme resource constraints of the ICD while maintaining high detection accuracy. We further propose a meta-learning algorithm with a novel patient-wise training tasks formatting strategy to personalize the 1D-CNN. The algorithm generates a well-generalized model initialization containing across-patient knowledge, and performs a quick adaptation of the model to the specific patient's IEGMs. In this way, a new patient could be immediately assigned with personalized 1D-CNN model parameters using limited input data. Compared with the conventional VAs detection method, the proposed method achieves 2.2% increased sensitivity for detecting VAs rhythm and 8.6% increased specificity for non-VAs rhythm.

Life-threatening ventricular arrhythmias (VA) are the leading cause of sudden cardiac death (SCD), which is the most significant cause of natural death in the US [6]. The implantable cardioverter defibrillator (ICD) is a small device implanted to patients under high risk of SCD as a preventive treatment. The ICD continuously monitors the intracardiac rhythm and delivers shock when detecting the life-threatening VA. Traditional methods detect VA by setting criteria on the detected rhythm. However, those methods suffer from a high inappropriate shock rate and require a regular follow-up to optimize criteria parameters for each ICD recipient. To ameliorate the challenges, we propose the personalized computing framework for deep learning based VA detection on medical IoT systems. The system consists of intracardiac and surface rhythm monitors, and the cloud platform for data uploading, diagnosis, and CNN model personalization. We equip the system with real-time inference on both intracardiac and surface rhythm monitors. To improve the detection accuracy, we enable the monitors to detect VA collaboratively by proposing the cooperative inference. We also introduce the CNN personalization for each patient based on the computing framework to tackle the unlabeled and limited rhythm data problem. When compared with the traditional detection algorithm, the proposed method achieves comparable accuracy on VA rhythm detection and 6.6% reduction in inappropriate shock rate, while the average inference latency is kept at 71ms.