用轻量级模型在设备端实时检测心律失常，兼顾精度与低功耗。
ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
- 基于量化自编码器的微型机器学习模型，参数极少适合嵌入式部署。
- 在ESP32-S3上实现9毫秒延迟、84%召回率，模型仅180KB。
- 适用于可穿戴设备，保护隐私且无需云端计算，适合医疗边缘场景。
本文提出一种基于自编码器的微型机器学习（TinyML）方法，用于在资源受限的嵌入式系统上实现心电图（ECG）分段与心律失常实时检测。我们设计了极简层结构与参数的INT8量化自编码器模型，支持在嵌入式设备上部署。模型在基于MIT-BIH心律失常数据库构建的自定义数据集上评估，涵盖超过95,000个ECG片段，在PC仿真与设备端均进行验证。实验在运行TensorFlow Lite Micro的ESP32-S3微控制器上完成。评估后开展详尽分析，包括按标注和记录级别的失败分析，以刻画模型在不同心电形态与节律模式下的表现，并解释漏检原因。部分看似误判的情况实为参考标注中被标记为正常的早期或细微异常，表明模型具备高敏感性。剔除模糊样本后，最优的基于DNN的自编码器达到84%召回率、79% F1分数，模型大小约180 KB，设备端推理延迟为9毫秒。结果证明，低功耗、隐私保护的嵌入式可穿戴系统可在本地完成精准心律失常检测。
原文摘要 · Abstract (English)
Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized autoencoder-based TinyML models with minimal layers and parameters for embedded deployment. These models are evaluated using a custom dataset derived from the MIT-BIH Arrhythmia Database and validated in both PC-based simulations and on-device environments. For the evaluations, over 95,000 ECG segments are processed on an ESP32-S3 microcontroller running the TensorFlow Lite Micro runtime. Post-evaluation, detailed analysis, including annotation-wise and record-wise failure analysis, is conducted to characterize model behavior across diverse ECG morphologies and rhythm patterns and to explain missed detections. In several cases, apparent misclassifications may correspond to early or subtle anomaly patterns labeled as normal in the reference annotations, highlighting the model's sensitivity. A refined evaluation by filtering out ambiguous cases in the dataset shows that the best-performing DNN-based autoencoder achieves a recall of 84%, an F1-score of 79%, a model size of approximately 180 KB, and an inference latency of 9 ms on-device. These results demonstrate the feasibility of low-power, privacy-preserving embedded wearable systems capable of performing accurate arrhythmia detection entirely on-device.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。