Gragnaniello Maria, Marrazzo Vincenzo Romano, Borghese Alessandro, Maresca Luca, Breglio Giovanni, Riccio Michele
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.
Bioengineering (Basel). 2024 Dec 24;12(1):4. doi: 10.3390/bioengineering12010004.
Diabetes is a chronic condition, and traditional monitoring methods are invasive, significantly reducing the quality of life of the patients. This study proposes the design of an innovative system based on a microcontroller that performs real-time ECG acquisition and evaluates the presence of diabetes using an Edge-AI solution. A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device. By applying quantization as an optimization technique, the model effectively balances memory usage and accuracy, achieving an accuracy of 89.52% with an average precision and recall of 0.91 and 0.90, respectively. These results were obtained with a minimal memory footprint of 347 kB flash and 23 kB RAM, showcasing the system's suitability for wearable embedded devices. Furthermore, a custom PCB was developed to validate the system in a real-world scenario. The hardware integrates high-performance electronics with low power consumption, demonstrating the feasibility of deploying Edge-AI for non-invasive, real-time diabetes detection in resource-constrained environments. This design represents a significant step forward in improving the accessibility and practicality of diabetes monitoring.
糖尿病是一种慢性疾病，传统的监测方法具有侵入性，会显著降低患者的生活质量。本研究提出了一种基于微控制器的创新系统设计，该系统可进行实时心电图采集，并使用边缘人工智能解决方案评估糖尿病的存在情况。一种基于频谱图的预处理方法与一维卷积神经网络（1D-CNN）相结合，可直接在设备上分析心电图信号。通过将量化作为一种优化技术应用，该模型有效地平衡了内存使用和准确性，在平均精度和召回率分别为0.91和0.90的情况下，实现了89.52%的准确率。这些结果是在347 kB闪存和23 kB随机存取存储器的最小内存占用情况下获得的，展示了该系统对可穿戴嵌入式设备的适用性。此外，还开发了一块定制印刷电路板，以在实际场景中验证该系统。该硬件将高性能电子设备与低功耗集成在一起，证明了在资源受限环境中部署边缘人工智能进行非侵入性实时糖尿病检测的可行性。这种设计在提高糖尿病监测的可及性和实用性方面迈出了重要一步。
