Automated EEG-based screening of depression using deep convolutional neural network

Acharya, U. Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat; Subha, D. P.

doi:10.1016/j.cmpb.2018.04.012

Cited by 460 publications

(217 citation statements)

References 51 publications

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“…Even though this review focuses on speech, many studies provided multimodel models trained on audio and video recordings such as those from AVEC competitions, in which some multimodal models reported improved performance as compared to unimodal models . Some studies combined these types of features with neurophysiological measures such as electroencephalography …”

Section: Discussionmentioning

confidence: 99%

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Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

250

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Section: Discussionmentioning

confidence: 99%

“…70,152 Some studies combined these types of features with neurophysiological measures such as electroencephalography. 153…”

Section: Multimodal Learningmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

250

268

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“…Recent research has also shown promising results for CNN-based EEG classification. In seizure detection (Acharya et al, 2018a;Ansari et al, 2018a), depression detection (Liu et al, 2017) and sleep stage classification (Acharya et al, 2018b;Ansari et al, 2018b), CNN have shown promising classification capabilities for EEG data. A CNN for EEG-based speech stimulus reconstruction was presented recently (de Taillez et al, 2017), showing that deep learning is a feasible alternative to linear decoding methods.…”

Section: Introductionmentioning

confidence: 99%

EEG-based detection of the locus of auditory attention with convolutional neural networks

Vandecappelle

Deckers

Das

et al. 2018

Preprint

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When multiple people talk simultaneously, the healthy human auditory system is able to attend to one particular speaker of interest. Recently, it has been demonstrated that it is possible to infer to which speaker someone is attending by relating the neural activity, recorded by electroencephalography (EEG), with the speech signals. This is relevant for an effective noise suppression in hearing devices, in order to detect the target speaker in a multi-speaker scenario. Most auditory attention detection algorithms use a linear EEG decoder to reconstruct the attended stimulus envelope, which is then compared to the original stimuli envelopes to determine the attended speaker. Classifying attention within a short time interval remains the main challenge. We present two different convolutional neural network (CNN)-based approaches to solve this problem. One aims to select the attended speaker from a given set of individual speaker envelopes, and the other extracts the locus of auditory attention (left or right), without knowledge of the speech envelopes. Our results show that it is possible to decode attention within 1-2 seconds, with a median accuracy around 80%, without access to the speech envelopes. This is promising for neuro-steered noise suppression in hearing aids, which requires fast and accurate attention detection. Furthermore, the possibility of detecting the locus of auditory attention without access to the speech envelopes is promising for the scenarios in which per-speaker envelopes are unavailable. It will also enable establishing a fast and objective attention measure in future studies. Index TermsConvolutional neural networks (CNN), auditory attention detection (AAD), electroencephalography (EEG), neurosteered auditory prosthesis, brain-computer interface (BCI)

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“…Mumtaz et al, based on resting-state EEG using SVM for classification, achieved an accuracy of 98.4%, sensitivity of 96.66%, and specificity of 100% [9]. Acharya et al used deep convolution neural network to recognize depression based on EEG signals and achieved the highest accuracy of 96% [10].…”

Section: Introductionmentioning

confidence: 99%

Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

Cao

Zhai

et al. 2020

Complexity

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is paper presents a method of depression recognition based on direct measurement of a ective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. en, in order to eliminate noise and redundant information and obtain better classi cation features, statistical methods (FDR corrected ttest) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. e results show that, on the one hand, the classi cation performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classi cation results.is study is of great value for the establishment of an automatic depression diagnosis system for clinical use.

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Automated EEG-based screening of depression using deep convolutional neural network

Cited by 460 publications

References 51 publications

Automated assessment of psychiatric disorders using speech: A systematic review

Automated assessment of psychiatric disorders using speech: A systematic review

EEG-based detection of the locus of auditory attention with convolutional neural networks

Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

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