使用基于卷积神经网络的临床和EEG特征对耐药性的早期预测
Shijun Yang1, Shanshan Li2, Hanlin Wang3
1Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China.
Seizure
|December 20, 2023
概括
机器学习模型使用脑电图 (EEG) 和临床数据预测耐药性 (DRE). 综合临床-EEG模型显示,在新诊断的患者中识别DRE风险的准确性很高.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 影响全球数百万人,其中很大一部分患有耐药性 (DRE).
- 早期预测DRE对于及时干预和改善患者结果至关重要.
- 机器学习 (ML) 为分析诸如脑电图 (EEG) 等复杂的神经数据提供了有前途的工具.
研究的目的:
- 开发和验证两个一维卷积神经网络 (CNN) 算法.
- 在新诊断的患者中预测耐药性 (DRE).
- 为了利用EEG信号和临床特征来提高预测准确度.
主要方法:
- 利用了101名患者的1010个EEG信号时代和15个临床特征.
- 开发了两种1D CNN算法,一个是专注于EEG数据,另一个是联合临床-EEG模型.
- 采用了80:20的开发验证分割和十倍的交叉验证.
- 使用准确度,特异性,精度,灵敏度,F1分数和AUC等指标评估性能.
主要成果:
- 临床EEG模型在验证组中实现了高性能,AUC为0.81,准确度为0.84.
- 仅使用EEG模型的AUC为0.76,验证准确度为0.81.
- 两种模型都显示出强大的预测能力,临床EEG模型在测试组的关键指标上表现出卓越的表现.
结论:
- 临床EEG模型显示,在新诊断的患者中,预测DRE具有显著的潜力.
- 这种方法可以帮助更早地识别高风险个体,促进迅速调整治疗.
- 该研究强调了将ML与临床和EEG数据集成为管理的价值.
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