High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis

Qayyum, Abdul; Razzak, Muhammad Imran; Tanveer, M.; Mazher, Moona; Alhaqbani, Bandar

doi:10.1109/tcbb.2023.3257175

Cited by 14 publications

(10 citation statements)

References 32 publications

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“…In addition, multimodal models are also one of the hotspots of research. Qayyum et al combined audio spectrograms and multiple frequencies of EEG signals to improve the diagnostic performance 18 . Fang et al and Xia et al use three modalities, namely video, audio, and text, for their respective studies.…”

Section: Related Work Of Depression Recognitionmentioning

confidence: 99%

“…In the multi-classification task, the severity of depression was divided into four levels: no depression, mild depression, moderate depression, and severe depression. The PHQ-8 score (range 0-24) was discretized into 4 categories: [0-4], [5][6][7][8][9] , [10][11][12][13][14] and [15][16][17][18][19][20][21][22][23][24], and these four categories are labeled as non, mild, moderate and severe, respectively.…”

Section: Experimental Tasksmentioning

confidence: 99%

“…And it typically requires a wealth of professional knowledge as support.Fortunately, artificial intelligence technology can utilize big data and intelligent algorithms to provide earlier diagnosis and intervention for the treatment of depression. Numerous methods have been proposed, encompassing biomarker-based approaches 5 , social media data-based techniques 6,7 , video-based methodologies 8,9 , audiobased methods [10][11][12][13] , electroencephalogram(EEG)-based approaches 14,15 , galvanic skin-based techniques 16,17 , and multimodal fusion strategies [18][19][20][21] . Within these approaches, there are invasive and non-invasive approaches.…”

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confidence: 99%

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Depression recognition using voice-based pre-training model

Huang,

Wang,

Gao

et al. 2024

Sci Rep

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The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

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Section: Related Work Of Depression Recognitionmentioning

confidence: 99%

Section: Experimental Tasksmentioning

confidence: 99%

mentioning

confidence: 99%

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Depression recognition using voice-based pre-training model

Huang,

Wang,

Gao

et al. 2024

Sci Rep

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“…EEG signals have several advantages over other physiological indicators when it comes to the detection of depression. Prior research works have indicated that EEG can be employed as an effective instrument in the detection and assessment of depression [19][20][21][22]. EEG measures the activity of the brain directly by recording the electrical signals produced by neurons.…”

Section: Introductionmentioning

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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“…Research groups have developed tools using various types of data modality, validated in numerous mental health populations, including depression [20], [21], anxiety disorder [22], schizophrenia [23], posttraumatic stress disorder (PTSD) [24], and almost all common psychiatric disorders. Diverse modalities of signals have been investigated, including behavioral signals, such as facial and body movements [20], [21], [25], speech acoustics [26]- [28], verbal or written content [29], sleep [30] and activity [24], [31] patterns, as well as physiological signals such as cardiovascular (heart rate [24], [32], electrocardiogram [28], [33], etc. ) and neural signals (electroencephalogram [34], [35], functional magnetic resonance imaging [36], [37] and functional near-infrared spectroscopy [38]).…”

Section: Introductionmentioning

confidence: 99%

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

Jiang,

Seyedi,

Griner

et al. 2023

Preprint

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ObjectiveThe current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders.MethodsTime series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination.ResultsStatistically significant feature differences were found between controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models.ConclusionMultimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status.SignificanceThe proposed multimodal approach has the potential to facilitate objective, remote, and low-cost assessment for low-burden automated mental health services.

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High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis

Cited by 14 publications

References 32 publications

Depression recognition using voice-based pre-training model

Depression recognition using voice-based pre-training model

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

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

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