Keivanimehr Ali Reza, Akbari Mohammad
Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Department of Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Comput Biol Med. 2025 Mar;186:109653. doi: 10.1016/j.compbiomed.2025.109653. Epub 2025 Jan 10.
Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks. Following this, we delve into the methodologies of knowledge distillation, quantization, and pruning, which represent the cornerstone strategies for optimizing machine learning models to operate efficiently within resource-constrained environments. Furthermore, our discussion extends to the role of efficient deep neural networks tailored specifically for cardiovascular monitoring on wearable devices with limited computational resources. Through a comprehensive review, we analyze the applications of prominent artificial neural network architectures including Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), and Transformers in the domain of Electrocardiogram (ECG) analytics, shedding light on their efficacy and potential in advancing healthcare technology.
微型机器学习（TinyML）和边缘智能已成为在位于网络最边缘的资源受限设备上实现机器学习的关键范式。在本文中，我们探讨了TinyML在利用可穿戴设备作为主要接口促进对心脏异常患者进行普及性、低功耗心血管监测和实时分析方面的变革潜力。首先，我们概述了TinyML的软件和硬件支持因素，并研究了诸如低功耗广域网（LPWAN）等网络解决方案，这些方案有助于TinyML框架的无缝部署。在此之后，我们深入探讨知识蒸馏、量化和剪枝的方法，这些方法是在资源受限环境中优化机器学习模型以高效运行的基石策略。此外，我们的讨论还扩展到专门为计算资源有限的可穿戴设备上的心血管监测量身定制的高效深度神经网络的作用。通过全面综述，我们分析了包括卷积神经网络（CNN）、自动编码器、深度信念网络（DBN）和Transformer在内的著名人工神经网络架构在心电图（ECG）分析领域的应用，揭示了它们在推动医疗技术发展方面的功效和潜力。
