Monitoring Mental Heal TH Using Physiological Signals

Some of the indicators of suicidal tendencies are mental health issues like Anxiety and Depression, ADHD, PTSD, etc. Even though there are doctors who help to stabilize these conditions, it is difficult for them to analyze the individual's daily emotional state and tendencies. This may lead to difficulties in diagnosis and may also affect the treatment of the individuals. To overcome this problem, a monitoring system which routinely monitors the mental state of the individual and also detects if there are any signs or symptoms of serious mental health issues is required. The Proposed system uses input peripheral data like GSR data, ECG signal of the individuals respectively. The ECG and GSR data are run through a regression system which predicts several indicators for classification like, valence, arousal, dominance, familiarity and liking. The original data along with the predicted data are collected and input into a classification algorithm like Support Vector Machine (SVM) algorithm. The obtained classified emotion is then sent to an inference engine which with each input of emotion infers if the individual has any mental health condition like Anxiety disorder and Depression.