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在医疗保健决策支持系统中用于发作检测的混合元启发框架

Indu Dokare^1,2, Sudha Gupta³

¹Department of Electronics Engineering, K. J. Somaiya School of Engineering (formerly K. J. Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai, 400077, Maharashtra, India. indu.dokare@somaiya.edu.

Acta epileptologica

|August 31, 2025

概括

这项研究引入了一种混合的元启发方法,用于使用群优化 (ACO) 和灰狼优化 (GWO) 和随机森林分类器自动检测发作. 该方法在分析EEG数据以实现个性化的管理方面具有高准确性,灵敏性和特异性.

科学领域:

计算机智能和机器学习应用于生物医学信号处理.
医疗信息学和自动诊断工具的开发.

背景情况:

发作的检测对于患者的护理至关重要,需要精确可靠的方法.
自动抓捕检测系统从计算智能和优化技术中受益.

研究的目的:

开发一种新的混合方法来检测患者特异性的发作.
通过优化算法提高自动发作检测的性能.

主要方法:

一种混合方法,用于特征选择的群优化 (ACO) 和用于随机森林 (RF) 超参数调整的灰狼优化 (GWO).
使用CHB-MIT和Seina数据集的多通道EEG数据进行评估.
使用精度,灵敏度和特异性指标进行性能评估.

主要成果:

在CHB-MIT数据集上,ACO-GWO-RF管道实现了高性能 (96.70%的准确性,92.66%的灵敏性,99.24%的特异性).
塞纳数据集的准确度为93.01%,灵敏度为89.82%,特异性为96.26%.
混合方法在处理复杂的EEG数据方面表现出强性,并且表现优于现有的方法.

结论:

这种混合的元启发方法有效地优化了EEG数据的处理和分类以检测发作.
这种方法的强大,患者特异性性能表明了互动健康应用和个性化管理的潜力.

关键词:

群的优化电脑脑电图信号发生发作灰狼优化医疗保健决策支持系统随机森林

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