Computer-Aided Diagnosis of Depression Using EEG Signals

Acharya, U. Rajendra; Sudarshan, Vidya K.; Adeli, Hojjat; Santhosh, Jayasree; Koh, Joel En Wei; Adeli, Amir

doi:10.1159/000381950

Cited by 159 publications

(70 citation statements)

References 82 publications

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“…These articles focused on EEG applications and how to use EEGs to diagnose and assess medical conditions. These articles also explored the relationships between symptoms and affective states, such as schizophrenia [17,18], depression [20], disorders of consciousness [19], and autism [21].…”

Section: Review Papermentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Review Papermentioning

confidence: 99%

“…A number of studies are based on the automated classification of normal and depression-related EEG signals. This proposed automatic classification system could serve as a useful diagnostic and monitoring tool for the detection of depression [20,79,133,135,226,228,231].…”

Section: Othermentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…The resulting technologies aim to help patients monitor their own conditions while also supporting carer-providers. Examples include the detection and monitoring of stress [47,62,68,74,83], classification of different emotional states [18,20,40,41], depression monitoring [7,37,73], obsessive compulsive disorder [12], behaviour classification [55], or cardiac states [48,49,65].…”

Section: Related Workmentioning

confidence: 99%

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos

Sandulescu

Andrews

et al. 2016

Int. J. Neur. Syst.

150

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2 Mozos et al.Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.

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“…Recent developments in biomedical engineering provided us with new mental disorder diagnosis support systems [39][40][41][42]. These systems detect symptoms of depression, autism and alcoholism in an early stage of the disease when therapeutic methods, such as bright light therapy, is most effective.…”

Section: Discussionmentioning

confidence: 99%

Formal and Model Driven Design of the Bright Light Therapy System Luxamet

Faust

2016

J. Mech. Med. Biol.

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This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. FAUST, Oliver and YU, Wenwei (2015). Formal and model driven design of the bright light therapy system Luxamet. Journal of Mechanics in Medicine and Biology, 16 (05), p. 1650065. Published version Copyright and re-use policySee http://shura.shu.ac.uk/information.html Sheffield Hallam University Research Archivehttp://shura.shu.ac.uk Seasonal depression seriously diminishes the quality of life for many patients. To improve their condition, we propose LUXAMET, a bright light therapy system. This system has the potential relieve patients from some of the symptoms caused by seasonal depression. The system was designed with a formal and model driven design methodology. This methodology enabled us to minimize systemic hazards, like blinding patients with an unhealthy dose of light. This was achieved by controlling race conditions and memory leaks, during design time. We proof that the system specification is deadlock as well as livelock free and there are no invariant violations. These proves, together with the similarity between specification model and implementation code, make us confident that the implemented system is a reliable tool which can help patients during seasonal depression.

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Computer-Aided Diagnosis of Depression Using EEG Signals

Cited by 159 publications

References 82 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Stress Detection Using Wearable Physiological and Sociometric Sensors

Formal and Model Driven Design of the Bright Light Therapy System Luxamet

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