EDA as a Discriminate Feature in Computation of Mental Stress

In computation of mental stress, various features are determined from a range of physiological signals. During stress, hormones levels inside the body of a stressed person are changed that results in a number of biomedical signals that are communicated among different body organs. A wireless wearable platform has been designed that record these biomedical signals. To induce stress, a series of cognitive experiments were developed that produce stress on the participants. EDA, HRV, respiration and brain signals are used for computing features and the objective was to identify most significant feature or their various combinations. It is verified that EDA features achieves a similar accuracy that can be obtained using various combination of features or using a master set containing all the features. The classification accuracy is more than 80% using EDA with a SVM model containing rbf kernel.