基于嵌入式AI的癫痫发作监测系统实现

Implementation of seizure monitoring system based on embedded AI

癫痫监测旨在防止患者在癫痫发作期间因失去意识而可能经历的事故。通过分析脑电信号来进行癫痫实时监测,从而为癫痫的诊断、治疗和评估提供相应的参考。本研究设计了一款基于嵌入式AI的癫痫发作监测系统,分为3部分:训练模块、测试模块和报警模块。其中,训练模块使用波恩数据集,采用小波包分解和一维卷积神经网络进行训练,最终模型准确率高达98.3%;测试模块使用脑电波传感器采集信号,通过蓝牙传输,经单片机处理后与训练模型比对;报警模块是将上述结果反馈至微信小程序,如若异常及时报警。该系统基于嵌入式AI,采用可穿戴式癫痫监测报警设备,具有实时监测癫痫发作的功能,能减小患者受到的伤害,保护患者安全。

Epilepsy monitoring aims to prevent accidents that patients may experience due to unconsciousness during epileptic seizures.Epilepsy can be monitored in real time by analyzing EEG signals,so as to provide corresponding references for the diagnosis,treatment and evaluation of epilepsy.A seizure monitoring system based on embedded AI is designed in the study,and the system is divided into 3 modules,namely training module,test module and alarm module.Born data set is adopted in training module,and wavelet packet decomposition and one-dimensional convolutional neural network are used for training.The final accuracy of the model is up to 98.3%.The test module uses brainwave sensor to collect signals which are transmitted through Bluetooth,and then compared with the training model after the test model is processed by MCU.The alarm module will feedback the above results to the WeChat applet,and alarm in time if there is any abnormality.The system designed based on embedded AI can reduce the injury to patients and protect the safety of patients for it adopts wearable epilepsy monitoring and alarm equipment and is capable of real-time monitoring of epileptic seizures.