Model Construction of Using Physiological Signals to Detect Mental Health Status

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.