Naregalkar P R, Shinde A A, Patil M V
Electronics & Communication, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.
MethodsX. 2025 May 7;14:103358. doi: 10.1016/j.mex.2025.103358. eCollection 2025 Jun.
Mental health disorders like stress, anxiety, and depression are increasing rapidly these days. Diagnosis of mental health disorders is a matter of concern in this era. A cost-effective and efficient method is to be implemented for detection. With this aim, stress is being monitored in this work with the help of physiological signals. This work uses a machine learning approach to detect a subject in stressed and non-stressed situations. This work aims to automatically detect stressful situations in humans using physiological data collected during anxiety-inducing scenarios. Diagnosing stress in the early stage can be helpful to minimize the risk of stress-related issues and enhance the overall well-being of the patient. The traditional methods for diagnosing stress are based on patient reporting. This approach has limitations. This proposed research aims to develop a stress-assessing model with a machine learning approach.•Stress and anxiety these days have become a prevalent issue affecting individuals' well-being and productivity. Early detection of these conditions is crucial for timely intervention and prevention of associated health complications. This paper presents a machine learning model for stress diagnosis.•The dataset consists of recordings obtained from individuals under different stress levels. The physiological signals used in this project are ECG, EMG, HR, RESP, Foot GSR, and Hand GSR. The machine learning algorithms, like Decision tree and kernel support vector machine, are employed for dope classification tasks. Additionally, a deep learning framework based on feed-forward artificial neural networks is introduced for comparative analysis.•The study evaluates the accuracies of both binary (Stressed Vs. non-Stressed) and three-class (relaxed Vs. baseline Vs. stressed) classification. Results demonstrate promising accuracies with machine learning techniques achieving up to 91.87 % and 66.68 % for binary classes and three classifications respectively. This paper highlights the potential of machine learning methods accurately detecting mental disorders offering insights for the development of effective detection managing tools.
如今，诸如压力、焦虑和抑郁等心理健康障碍正在迅速增加。在这个时代，心理健康障碍的诊断是一个令人担忧的问题。需要实施一种经济高效的方法来进行检测。出于这个目的，本研究借助生理信号来监测压力。这项工作使用机器学习方法来检测处于压力和非压力状态下的受试者。本研究旨在利用在诱发焦虑场景中收集的生理数据自动检测人类的压力状况。在早期阶段诊断压力有助于将与压力相关问题的风险降至最低，并提高患者的整体幸福感。传统的压力诊断方法基于患者报告。这种方法存在局限性。本研究旨在通过机器学习方法开发一种压力评估模型。
•如今，压力和焦虑已成为影响个人幸福感和生产力的普遍问题。早期发现这些状况对于及时干预和预防相关健康并发症至关重要。本文提出了一种用于压力诊断的机器学习模型。
•数据集由从处于不同压力水平的个体获得的记录组成。本项目中使用的生理信号包括心电图（ECG）、肌电图（EMG）、心率（HR）、呼吸（RESP）、足部皮肤电反应（Foot GSR）和手部皮肤电反应（Hand GSR）。决策树和核支持向量机等机器学习算法被用于分类任务。此外，还引入了一种基于前馈人工神经网络的深度学习框架进行对比分析。
•该研究评估了二元分类（压力状态与非压力状态）和三类分类（放松状态与基线状态与压力状态）的准确率。结果表明，机器学习技术在二元分类和三类分类中分别取得了高达91.87%和66.68%的准确率，前景可观。本文强调了机器学习方法在准确检测精神障碍方面的潜力，为开发有效的检测管理工具提供了见解。
