The Diagnosis of Mental Stress by Using Data Mining Technologies

Abstract

In today’s fast-paced and competitive environment, mental stress has become a part and parcel of our daily life. However, mental stress can have serious effects on both our psychological and physical health. People under long-term stress can cause mental disorders, and cardiovascular disease. Moreover, people often ignore the symptoms of stress from their own bodies. Therefore, many chronic disease and mental illness are more and more serious gradually and damaging their body. The prior studies are interest in the diagnosis of metal stress. Some physiological parameters are used for the diagnosis of mental stress. However, these parameters pattern recognition is a difficult problem due to they have a time varying morphology subject to physiological conditions and the presence of noise. Therefore, how to capture and analyze personal physiological signals to assessment of mental stress under different conditions is a recurrent issue in many engineering and medicine fields. In addition, it is also important how to provide appropriate ways for stress relief under different the mental stress level. This study will evaluate different classification methods and understand which one is appropriate to detect the mental stress. Three data mining technologies are used to detect the mental stress level and have an experiment to evaluate the performance of the mental stress diagnosis. The heart rate, blood pressure, heart rate variability and autonomic nervous system are used to assess the level of mental stress. It might be helpful to assess mental condition in clinical practice.