MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals

Lin, Hongtuo; Jian, Chufan; Cao, Yang; Ma, Xiaojun; Wang, Hailiang; Miao, Fen; Fan, Xiaomao; Yang, Jinzhu; Zhao, Gansen; Zhou, Hui

doi:10.1016/j.compbiomed.2021.105039

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…Nearly half of the models (48.3% [268 of 555]) were found in studies authored by those with academic training in computers and data science (eTable 2 in Supplement 1). Schizophrenia (25.4% [141 of 555 models])…”

Section: Resultsmentioning

confidence: 99%

“…In the predictors domain, 187 of 555 models (33.7%; 95% CI, 29.9%- 37.6%) were rated with high ROB (Table 1). Defining predictors by knowing the outcome of these models was the unique source of the high ROB in this domain (ie, signaling question 2.2: were predictor assessments made without knowledge of outcome data?).…”

Section: Resultsmentioning

confidence: 99%

“…Defining predictors by knowing the outcome of these models was the unique source of the high ROB in this domain (ie, signaling question 2.2: were predictor assessments made without knowledge of outcome data?). In the outcome domain, high ROB was scored for 198 of 469 models (35.7%; 95% CI, 31.8%-39.7%) (Table 1). These models had a high ROB because the outcome knowledge of testing data sets was leaked into the predictors of the training set (ie, signaling question 3.3: were predictors excluded from the outcome definition?…”

Section: Resultsmentioning

confidence: 99%

“…High concern for clinical applicability in the participants domain was found for 2 of 555 models (0.4%; 95% CI, 0.00%-0.01%) . Furthermore, high concerns regarding clinical applicability were found for 199 of 555 models (35.9%; 95% CI, 31.9%-39.9%) in the predictors domain and for 217 of 555 models (39.1%; 95% CI, 35.0%-43.2%) in the outcome domain …”

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%

Section: Resultsmentioning

confidence: 99%

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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“…The current study implies that increased adaptation to conflict over sustained attention periods was accompanied among HAs by a greater need for cognitive resources in the conflict-processing stage. In accordance with the compensation hypothesis of neural circuit utilization [ 83 , 84 ], the PFC cortical areas of lowlanders exposed to high altitude may have been more strongly activated during the GSAT, in order to compensate for decreased oxygen availability on the plain.…”

Section: Discussionmentioning

confidence: 56%

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

et al. 2022

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(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

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The Dual Mahalanobis-kernel LSSVM for Semi-supervised Classification in Disease Diagnosis

Cui,

Xia,

Lang

et al. 2024

Arab J Sci Eng

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MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals

Cited by 9 publications

References 22 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

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

The Dual Mahalanobis-kernel LSSVM for Semi-supervised Classification in Disease Diagnosis

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