Hizem Moez, Aoueileyine Mohamed Ould-Elhassen, Belhaouari Samir Brahim, El Omri Abdelfatteh, Bouallegue Ridha
Innov'COM Laboratory, Higher School of Communication of Tunis, University of Carthage, Tunis, Tunisia.
Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Biomed Eng Comput Biol. 2025 Aug 10;16:11795972241283101. doi: 10.1177/11795972241283101. eCollection 2025.
Tiny Artificial Intelligence (Tiny AI) is transforming resource-constrained embedded systems, particularly in e-health applications, by introducing a shift in Tiny Machine Learning (TinyML) and its integration with the Internet of Things (IoT). Unlike conventional machine learning (ML), which demands substantial processing power, TinyML strategically delegates processing requirements to the cloud infrastructure, allowing lightweight models to run on embedded devices. This study aimed to (i) Develop a TinyML workflow that details the steps for model creation and deployment in resource-constrained environments and (ii) apply the workflow to e-health applications for the real-time detection of epileptic seizures using electroencephalography (EEG) data. The methodology employs a dataset of 4097 EEG recordings per patient, each 23.5 seconds long, from 500 patients, to develop a robust and resilient model. The model was deployed using TinyML on microcontrollers tailored to hardware with limited resources. TensorFlow Lite (TFLite) efficiently runs ML models on small devices, such wearables. Simulation outcomes demonstrated significant performance, particularly in predicting epileptic seizures, with the ExtraTrees Classifier achieving a notable 99.6% Area Under the Curve (AUC) on the validation set. Because of its superior performance, the ExtraTrees Classifier was selected as the preferred model. For the optimized TinyML model, the accuracy remained practically unchanged, whereas inference time was significantly reduced. Additionally, the converted model had a smaller size of 256 KB, approximately ten times smaller, making it suitable for microcontrollers with a capacity of no more than 1 MB. These findings highlight the potential of TinyML to significantly enhance healthcare applications by enabling real-time, energy-efficient decision-making directly on local devices. This is especially valuable in scenarios with limited computing resources or during emergencies, as it reduces latency, ensures privacy, and operates without reliance on cloud infrastructure. Moreover, by reducing the size of training datasets needed, TinyML helps lower overall costs and minimizes the risk of overfitting, making it an even more cost-effective and reliable solution for healthcare innovations.
微型人工智能（Tiny AI）正在通过引入微型机器学习（TinyML）的转变及其与物联网（IoT）的集成，改变资源受限的嵌入式系统，尤其是在电子健康应用中。与需要大量处理能力的传统机器学习（ML）不同，TinyML策略性地将处理需求委托给云基础设施，使轻量级模型能够在嵌入式设备上运行。本研究旨在：（i）开发一种TinyML工作流程，详细说明在资源受限环境中创建和部署模型的步骤；（ii）将该工作流程应用于电子健康应用，使用脑电图（EEG）数据实时检测癫痫发作。该方法采用了来自500名患者的数据集，每位患者有4097条EEG记录，每条记录长23.5秒，以开发一个强大且有弹性的模型。该模型使用TinyML部署在针对资源有限的硬件定制的微控制器上。TensorFlow Lite（TFLite）能够在小型设备（如可穿戴设备）上高效运行ML模型。模拟结果显示出显著的性能，特别是在预测癫痫发作方面，ExtraTrees分类器在验证集上的曲线下面积（AUC）达到了显著的99.6%。由于其卓越的性能，ExtraTrees分类器被选为首选模型。对于优化后的TinyML模型，准确率基本保持不变，而推理时间显著减少。此外，转换后的模型大小为256 KB，约为原来的十分之一，适合容量不超过1 MB的微控制器。这些发现突出了TinyML通过在本地设备上直接实现实时、节能决策来显著增强医疗保健应用的潜力。这在计算资源有限的场景或紧急情况下尤其有价值，因为它减少了延迟，确保了隐私，并且无需依赖云基础设施运行。此外，通过减少所需训练数据集的大小，TinyML有助于降低总体成本并最小化过拟合风险，使其成为医疗保健创新更具成本效益和可靠性的解决方案。
