根据压缩的EEG信号来快速处理和分类发作
Achraf Djemal1, Ahmed Yahia Kallel2, Cherif Ouni1
1Professorship Measurement and Sensor Technology, Chemnitz University of Technology, Chemnitz, Germany; Laboratory of Signals, Systems, Artificial Intelligence and Networks, Digital Research Centre of Sfax, National School of Electronics and Telecommunications of Sfax, 3021 Sfax, Tunisia.
压缩传感 (CS) 显著减少了用于诊断的脑电图 (EEG) 数据大小,使便携式实时系统成为可能. 这种方法实现了高精度 (98.78%),同时减少了文件大小和能源消耗.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 神经学 神经学
背景情况:
- 根据脑电图 (EEG) 信号诊断是复杂的,并且由于信号的变化和体积,容易出现错误.
- 开发便携式实时诊断系统面临信号处理和准确分类方面的挑战.
研究的目的:
- 提出和评估压力传感 (CS) 对于高效的EEG信号凝聚和抓获分类.
- 为了证明一个便携式嵌入式系统的可行性,用于实时诊断.
主要方法:
- 压缩传感 (CS) 使用离散的等号变换 (DCT) 和随机矩阵乘法用于EEG信号压缩 (5-70%的比率).
- 基于相互信息和相关性矩阵的特征选择.
- XGBoost机器学习模型用于发作分类.
- 在STM32微控制器和Raspberry Pi上实现嵌入式系统演示.
主要成果:
- 通过XGBoost.实现了98.78%的分类准确性.
- 在70%的压缩:减少70%的文件大小,减少84%的传输时间,并大幅节省能源.
- 保持信号质量,PSNR为16.15±3.98,皮尔森相关系数为0.68±0.15.
结论:
- 压缩传感为减少EEG数据提供了一种有效的方法,用于便携式实时诊断.
- 拟议的系统可实现精确的,自动的扣押分类,显著提高效率和能源消耗.
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