A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism

Wang, Zhuozheng; Ma, Zhuo; Liu, Wei; An, Zhefeng; Huang, Fubiao

doi:10.3390/brainsci12070834

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were engaged to extract the features from the recorded EEG data and an accuracy of 89.63% is attained for the proposed framework. A 1D-CNN and GRU network-based deep learning model has been developed for depression detection using EEG data in a study conducted in [ 39 ]. An accuracy of 99.33% and 97.98% is achieved for depression detection on a public and private EEG-based dataset, respectively.…”

Section: Related Workmentioning

confidence: 99%

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

Khan,

Umar Saeed,

Frnda

et al. 2024

PLoS ONE

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Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

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Section: Related Workmentioning

confidence: 99%

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

Khan,

Umar Saeed,

Frnda

et al. 2024

PLoS ONE

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“…This suggests that CNN-LSTM outperforms traditional machine learning methods significantly. Wang Z et al [18] utilize a combination of One-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU) to extract both local and global features from the EEG signal. It mentions an insufficient number of negative samples in the dataset.…”

Section: Deep Learningmentioning

confidence: 99%

“…Ksibi A et al [19] developed three models, namely, XGBOOST, Random Forest and 1D CNN model with an overall accuracy of 97%. When assessing depression, it is advantageous to take into account demographic variables beyond age and gender, such as ethnicity and socioeconomic status [12,13,[17][18][19][20][21][22][23].…”

Section: Deep Learningmentioning

confidence: 99%

A Hybrid Neural Network for Electroencephalogram (EEG)-based Screening of Depression

Passi

2023

SBB

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Technological development is considered one of the major parts of this recent time as it helps to improve people's quality of life and resolve their issues and challenges faced in daily life. In recent times a happy life has been considered one of the major requirements for people as most people live under stress and face several mental disorders like depression, anxiety and loneliness. In the metal disorder space, depression is a major and common disease in recent society. According to the World Health Organization (WHO), it is estimated that 5% of adults suffer from depression. Diagnosis of depression has several challenges like time consuming patient counselling, over-dependence on doctors and accuracy of diagnosis. To resolve these diagnosis issues, computer aided system solution is required with the use of machine learning tool. The objective of this research is to develop hybrid deep learning model by using CNN and LSTM. The selected dataset which was used for this study contains a dataset of 945 subjects of mental disorders and healthy control subjects. Three hybrid models were developed and compared with different sets of extracted features. Raw data was pre-processed and applied in hybrid model and at the end model validated with the unknown EEG dataset. The hybrid model with entire features of dataset reported an accuracy of 98.0% and performed superior in comparison with other two models which trained with extracted features by using decision tree classifier. The results show that the developed hybrid CNN and LSTM model is accurate, less complex and useful in detecting mental disorders including depression using EEG signals.

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“…Their model reaches an accuracy of 89.63% in the classification of depression. A second study from the same laboratory [ 12 ], also using 16 channels, extended this solution by adding an attention layer and improving the results to 99.3%.…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States: A Data Driven Study

et al. 2022

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The growing number of depressive people and the overload in primary care services make it necessary to identify depressive states with easily accessible biomarkers such as mobile electroencephalography (EEG). Some studies have addressed this issue by collecting and analyzing EEG resting state in a search of appropriate features and classification methods. Traditionally, EEG resting state classification methods for depression were mainly based on linear or a combination of linear and non-linear features. We hypothesize that participants with ongoing depressive states differ from controls in complex patterns of brain dynamics that can be captured in EEG resting state data, using only nonlinear measures on a few electrodes, making it possible to develop cheap and wearable devices that could be even monitored through smartphones. To validate such a perspective, a resting-state EEG study was conducted with 50 participants, half with depressive state (DEP) and half controls (CTL). A data-driven approach was applied to select the most appropriate time window and electrodes for the EEG analyses, as suggested by Giacometti⁠, as well as the most efficient nonlinear features and classifiers, to distinguish between CTL and DEP participants. Nonlinear features showing temporo-spatial and spectral complexity were selected. The results confirmed that computing nonlinear features from a few selected electrodes in a 15 s time window are sufficient to classify DEP and CTL participants accurately. Finally, after training and testing internally the classifier, the trained machine was applied to EEG resting state data (CTL and DEP) from a publicly available database, validating the capacity of generalization of the classifier with data from different equipment, population, and environment obtaining an accuracy near 100%.

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A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism

Cited by 10 publications

References 43 publications

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

A Hybrid Neural Network for Electroencephalogram (EEG)-based Screening of Depression

Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States: A Data Driven Study

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