An End-to-End Depression Recognition Method Based on EEGNet

Liu, Bo; Chang, Hongli; Peng, Kang-Lin; Wang, Xuenan

doi:10.3389/fpsyt.2022.864393

Cited by 14 publications

(15 citation statements)

References 31 publications

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“…Approximately half of the models (258 of 555 [46.48%; 95% CI, 42.3%- 50.6%]) were assessed for poor reporting quality in the technique domain across 3 signaling questions . The unique source for the degraded reporting quality in this domain was the absence of algorithmic details, which limited reproducibility (eg, signaling question: were details for algorithm development reported?).…”

Section: Resultsmentioning

confidence: 99%

“…One-hundred fifty of 555 models (27.0%; 95% CI, 23.3%-30.7%) were rated with poor reporting quality in the applications domain across 2 signaling questions . Lacking intrinsic reports or discussions of the clinical implications of these models was the main source of the poor rating in this domain (eg, signaling question: were use cases and target conditions discussed?).…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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“…There are five evaluation indicators, i.e. Accuracy, Precision, Recall, F1-Score and Kappa coefficient [34].…”

Section: Model Evaluation and Evaluation Indexmentioning

confidence: 99%

“…The one-dimensional convolutional neural network and long short-term memory (1DCNN-LSTM) can automatically learn patterns in the effectively connected images constructed by EEG signals, and capture the spatiotemporal relationship existing in the brain connections, the accuracy rate for this method is 99.24% [32]. Acharya et al [33] and Liu et al [34] employed CNNs and EEGNet, respectively, which can automatically and adaptively classify between MDD and normal subjects without selecting feature sets semi-manually. Zhang et al pointed out that the brain function network of MDD patients showed a trend of randomization and a weakening of small world characteristics [35].…”

Section: Introductionmentioning

confidence: 99%

EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search

Chang¹,

Zong²,

Zheng³

et al. 2023

J. Neural Eng.

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Objective. Major Depressive Disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings’ mental activities and can be served as an objective biomarker for diagnosing MDD. Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels. Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition. Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.

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“…The EEG data of 40 depressed and 40 normal individuals were divided into training and test sets in the ratio of 7:3 for validating the classification performance of the model, which reached the accuracy of 94.69%. Liu et al applied the state-of-the-art DL model, named EEGNet [14], to the MODMA dataset containing 24 depressed patients and 29 normal people for the recognition of depression [15]. The LOSOCV method was adopted to train the model, which obtained the classification accuracy of 90.98%.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

Xia

Zhang

et al. 2023

IEEE Access

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Major depressive disorder (MDD) is a prevalent mental illness associated with abnormalities in structural and functional brain connectivity, and it has become a global public health problem. Early diagnosis is important and challenging for the treatment of MDD. Previous studies proposed the classification methods for MDD based on brain connectivity features through functional connectivity (FC) or effective connectivity (EC) measures. However, it requires prior knowledge and experience to manually select a algorithm to calculate the brain connectivity features. Given that the representation learning capabilities of deep learning (DL) models and the ability to capture correlations between data of self-attention mechanism, we proposed an end-to-end integrated DL model for classifying MDD patients and healthy controls (HCs) based on resting-state electroencephalography (EEG) data. This model first automatically learned the potential connectivity relationships among EEG channels through a multi-head self-attention mechanism, and then extracted higher-level features through a parallel two-branch convolution neural network (CNN) module, and finally completed the classification through a fully connected layer. A public resting-state EEG dataset was utilized to evaluate the validity of the proposed model. The experimental results indicated that the proposed model achieved 91.06% average accuracy that was better than those of comparison methods using the leave-one-subject-out cross-validation (LOSOCV) method. This study may provide a novel approach for brain connectivity modeling of MDD detection.

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An End-to-End Depression Recognition Method Based on EEGNet

Cited by 14 publications

References 31 publications

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

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