An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals. (December 2018)

• Record Type: Journal Article
• Authors: Sharma, Manish; Achuth, P.V.; Deb, Dipankar; Puthankattil, Subha D.; Acharya, U. Rajendra
• Is Part Of: Cognitive systems research. Volume 52(2018)
• Page Start: 508
• Page End: 520
• Publication Date: 2018-12
• Subjects: Depression -- Electroencephalogram (EEG) -- Three-channel orthogonal wavelet filter bank -- Bandwidth-duration localization; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.07.010
• Languages: English
• ISSNs: 1389-0417

Abstract

Highlights: A novel three-band time-frequency localized wavelet filter bank based automated depression detection. Only seven wavelet-based discriminating features are employed. Model attained classification accuracy of 99.58%. The model surpasses all existing models. Abstract: Depression is a mental illness. If not diagnosed and treated quickly, it can affect one's mood and quality of life. Modern life is stressful and fast paced, owing to which depression has emerged as a major source of mental health disorder. The electroencephalogram (EEG) signals, which are used to diagnose depression, are non-stationary, non-linear and complex. Their visual interpretation is difficult and takes time. This makes computer-aided depression diagnosis systems highly desirable for the early detection of the depression. This study aims towards the development of depression detection system using EEG based measures. We propose a computer aided depression diagnosis system using newly designed bandwidth-duration localized (BDL) three-channel orthogonal wavelet filter bank (TCOWFB) and EEG signal for the detection of depression. The EEG signal is decomposed into seven wavelet sub-bands (WSBs) using a optimal six-length TCOWFB. The logarithm of L 2 norm (LL2N) of six detailed WSBs and one approximate WSB are used as discriminating features.These features are used in the classification of normal or depression EEG signals by applying them to the least square support vector machine (LS-SVM). The proposed system attained the perfect value of 1 for area under the curve (AUC) of receiver's operating characteristics (ROC) using seven features. The proposed system with ten-fold cross validation (CV) strategy attained an average classification accuracy (ACA) of 99.58%. The proposed model obtained better ACA than the existing automated depression diagnosis systems (ADDS) and perfect AUC-ROC. Hence, it can be used in a clinical setup to diagnose the depression disorder accurately in lesser time, without any subjectivity due to human intervention.