978-1-4244-9352-4/11/$26.00 ©2011 IEEE 805 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) Physiological Signal Analysis for Patients with Depression Yen-Ting Chen 1 , I-Chung Hung 1* , Min-Wei Huang 23 , Chun-Ju Hou 1 , and Kuo-Sheng Cheng 2 1* Department of Electrical Engineering, Southern Taiwan University, Tainan, Taiwan 2 Institute of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan 3 Psychiatry Department, Chiayi Branch, Taichung Veterans General Hospital, Taiwan E-mail: hminwei@gmail.com Abstract—Depression is a common and serious mental disorder. About 1.9 million people in Taiwan are identified as having depression. There is a trend of increase of the prevalence of depression, three times more depressed persons within the past six years. A few patients with depression were in treatment. Therefore, an available algorithm will be build to measure the neurophysiology of depression. In this study, the physiological signals from depressed patients will be compared with those from normal people. This experiment use different pictures expresses happiness, sadness fear, and disgust to cause the emotion of subjects. The physiological signals of the patient is measured at the same time. The preliminary results show that the galvanic skin response, heart rate variability, and blood volume pulse of patients with depression is lower than the normal people does. More subjects will be evaluated in the project for investing the clinical significance. Keywords-Depression; Physiological Signal; Heart Rate Variability; Galvanic Skin Response I. INTRODUCTION There are many kinds of pressure living in present. Because the pressure become heavy, some people are living with depression. There are 15% patients with depression will suicide themselves. And there are 87% suicides diagnosed with depression during their lifetime. Suicide is the second cause of death between the age of 15 and 24, and the third cause of death between the age of 25 and 44, and the seventh cause of death between the age of 45 and 64. Moreover, the percentage of depressed patients in treatment were low. Even if the depressed patients were in treatment, doctors usually relied on their medical history and clinical observation [1]. Therefore, the purpose of this study is to develop an available algorithm to measure the neurophysiology of depression. The electrocardiogram, blood pressure, respiration, electromygram and skin temperature are as measures for clinical application [2~4]. Depression in Heart Rate Variability (HRV) measurement, usually shows low HRV and high ratio of parasympathetic sympathetic LF/HF [5]. In tension or in relaxation of a people will cause the variation of finger temperature and the difference of electric potential in muscle. The other physiology signal in this study for measurement is Blood Volume Pulse (BVP). BVP is about the flow of blood when a blood vessel is in vasoconstriction or vasodilatation in different emotion [6]. Therefore, the physiology parameters, like Electrocardiogram (ECG), Electromygram (EMG), Galvanic Skin Response (GSR), Finger Temperature (Temp), Blood Volume Pulse (BVP) will be used in estimating depression. This study will establish a trend analysis to investigate the relations of physical signals and emotions of patients with depressive disorder. According to the statistical inference from subjects to find out the standard for each parameter. II. MATERIALS AND METHODS The physiological signals of two groups of subjects, 16 normal persons and 10 patients, were measured. The protocol for this study was approved by the Institutional Review Board (IRB) and the informed consents were enrolled and agreed by all subjects. The emotional trigger films including some international emotional pictures (happiness, sadness, fear and disgust) from International Affective Pictures System (IAPS) were applied while physiological signals were measured at the same time. The flowchart for physiological signals analysis is as Fig. 1. First, the signals will be separated in different stimulated time segment. Second, the characteristics of every signals were evaluated for each part. Third, the comparisons of evaluated results in different time segments were performed for investigating the emotion variation. Finally, the statistical analysis to verify the experimental results. Figure 1. The flowchart for physiological signals analysis Heart rate variability is analyzed from ECG signals. And the way to calculate heart rate variability is Power Spectral Density (PSD) algorithm [8]. The algorithm is to find out the interval T between two adjacent R waveforms. The basic