Student Counseling and Mental Health Center, Beijing Wuzi University, Beijing 101149, China.
J Healthc Eng. 2021 Oct 20;2021:8544750. doi: 10.1155/2021/8544750. eCollection 2021.
Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health.
To design a mental health state detection model based on physiological signals to detect human mental health.
For the detection of mental health, the sliding window method is used to divide the physiological signal dataset and the corresponding time into several segments and then calculate the physiological signal data in the sliding window for each physiological signal to form a sequence of characteristic values; according to the heart rate variability of the physiological signal, the heart rate variability (HRV) is extracted from the interval spectrum waveform: through the discrete trend analysis in statistics, the change characteristics of the ECG signal are analyzed, and the sequence statistical indicators of the physiological signal are calculated. With the help of a support vector machine used for the significant accuracy with less computation power, the physiological signals of the mental state are classified, and the discriminant function of the mental health state signals is normalized. A mental health state detection model is constructed according to the index system, the optimal solution of the model is obtained through the optimization function, and the mental health state detection is completed.
The detection error of the proposed model is less which improves the detection accuracy and is less time consuming.
The detection model using physiological signals is proposed to evaluate the mental health status. As compared to the other detection models, its detection time is short and method error is always less than 2% which shows its accuracy and effectiveness.
心理健康是人类心理活动的直接指标，它也影响着人体的各个方面。它在监测人类心理健康方面起着非常重要的作用。
设计一种基于生理信号的心理健康状态检测模型，以检测人类的心理健康。
对于心理健康的检测，采用滑动窗口的方法将生理信号数据集和相应的时间划分为多个段，然后计算滑动窗口中每个生理信号的生理信号数据，形成特征值序列；根据生理信号的心率变异性，从间隔频谱波形中提取心率变异性（HRV）：通过统计中的离散趋势分析，分析 ECG 信号的变化特征，并计算生理信号的序列统计指标。借助支持向量机用于具有较少计算能力的显著准确性，对精神状态的生理信号进行分类，并对心理健康状态信号的判别函数进行归一化。根据指标体系构建心理健康状态检测模型，通过优化函数获得模型的最优解，完成心理健康状态检测。
该模型的检测误差较小，提高了检测精度，且耗时较少。
提出了一种基于生理信号的检测模型来评估心理健康状况。与其他检测模型相比，其检测时间短，方法误差始终小于 2%，表明其准确性和有效性。
