基于神经成像模式的机器学习和深度学习技术的全面审查,用于基于神经成像模式的发作检测和预测
Khadija Slama1, Ali Yahyaouy2, Jamal Riffi2
1Laboratory of Computer Science, Innovation, and Artificial Intelligence, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco. khadija.slama@usmba.ac.ma.
Visual computing for industry, biomedicine, and art
|December 10, 2025
概括
使用脑电图 (EEG) 检测和预测发作对于耐药性至关重要. 本综述考察了计算技术,机器学习 (ML) 和深度学习 (DL) 以改善发作的检测和预测.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
背景情况:
- 是一种慢性神经系统疾病,经常出现发作.
- 耐药性缺乏向治疗,许多患者没有接受治疗.
- 可靠的发作检测和预测对于临床管理至关重要.
研究的目的:
- 审查用于发作检测和预测的计算技术.
- 分析应用于脑波信号的机器学习 (ML) 和深度学习 (DL) 算法.
- 确定基于EEG的发作检测的趋势,挑战和未来方向.
主要方法:
- 对信号处理,ML和DL技术进行系统审查,以检测发作.
- 分析电脑电图 (EEG) 和其他神经成像模式.
- 传统和先进的计算方法的分类.
主要成果:
- 从传统的信号处理到先进的ML和DL算法的演变.
- 确定基于EEG的发作检测的主要研究趋势.
- 突出关键挑战,如数据稀缺性和模型通用性.
结论:
- 基于EEG的发作检测和预测方法已经显著进步.
- 需要进一步的研究来提高准确性,可解释性和临床适用性.
- 本综述为管理的未来发展提供了全面的资源.
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