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使用机器学习对基于生理学的焦虑检测进行系统审查

Shikha Shikha¹, Divyashikha Sethia², S Indu³

¹Computer Science and engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi-110042, New Delhi, New Delhi, Delhi, 110042, INDIA.

Biomedical physics & engineering express

|May 9, 2025

概括

这篇评论探讨了使用机器学习与生理信号,如脑电图来检测焦虑障碍. 它突出了可穿戴设备,并提出了一种多模式的方法,以更好地分类焦虑.

科学领域:

神经科学和计算精神病学
专注于大脑活动,生理反应和人工智能的交集,用于心理健康诊断.

背景情况:

由于复杂的症状,诊断焦虑症是具有挑战性的,导致治疗延迟.
非侵入性的生理信号为客观的焦虑评估提供了一个有希望的途径.

研究的目的:

系统地审查生理传感器和机器学习 (ML) 以诊断和预测焦虑障碍.
使用ML模型探索生理特征和焦虑之间的关系.
为增强焦虑分类提出一种新的多式联络方法.

主要方法:

对生理传感器 (EEG,ECG,EMG,EDA,呼吸) 和ML技术进行系统的文献综述.
在焦虑检测研究中使用的可穿戴设备的分析.
探索ML模型在将生理数据与焦虑相关的性能.

主要成果:

机器学习有效地从生理信号中识别焦虑模式.
可穿戴设备越来越多地用于远程和持续的焦虑监测.
结合各种生理信号的多模式方法显示了提高准确性的潜力.

结论:

与生理信号的ML集成为客观焦虑检测提供了一个可行的途径.
需要进一步的研究来应对数据标准化和模型通用性的挑战.
拟议的多式联络战略可以显著推进焦虑障碍的诊断和管理.

关键词:

深度学习 (Deep Learning) 是一种深度学习.机器学习机器学习生理信号生理信号压力和焦虑压力和焦虑可穿戴式传感器穿戴式传感器

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