Depression Identification by using PHQ-9 and SVM Classifier

Conference: McGraw-Hill International Conference on Signal, Image Processing Communication and Automation
Author(s): Mallikarjun H M, P Manimegalai, H N Suresh Year: 2017
Grenze ID: 02.MH-ICSIPCA.2017.1.65 Page: 425-431

Abstract

Depression is one of the deadliest ailments hitting about a noteworthy populace around the world This work\nproposes a method to identify Depression in subject by using EEG database recorded on different people of different age\ngroups and social organizations. by asking PHQ-9 score is computed for each subject. EEG frequency bands values are\ntabulated by using Bluetooth based Neurosky\'s Mindwave kit. In this work focus is to identify depression by Support Vector\nMachine (SVM). 47 Samples are set up by making inquiries from standard poll with a Wright and wrong replies in a diverse\nera from the individual in wearable head unit. 39 samples are trained and 8 are tested. In this work SVM classifier’s\nconfusion matrix is derived by MATLAB program and accuracy of 75 % is achieved.

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MH-ICSIPCA - 2017