Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG

Abstract

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.

Keywords: IoT; TinyML; e-health; electroencephalography; embedded systems; epileptic seizure; machine learning.

Figure 1.

Internet of medical things (Created with Biorender.com).

Figure 2.

ML workflow for data collection, training, and deployment (Created with Biorender.com).

Figure 3.

Transition from ML to TinyML (Created with Biorender.com), adapted with modifications from Aoueileyine.

Figure 4.

Model conversion for TinyML deployment workflow.

Figure 5.

Classifier evaluation and algorithm choice.

Figure 6.

AUC learning curve for ExtraTrees classifier.

Figure 7.

Effect of max features on AUC.

Figure 8.

ROC curve.

Figure 9.

Framework for deploying TinyML solutions (Created with Biorender.com), adapted with modifications from Aoueileyine.

Figure 10.

Real-time inferences on constrained performance devices (Raspberry Pi) (Created with Biorender.com).