Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data

Ding, Xinfang; Yue, Xinxin; Zheng, Rui; Bi, Chen; Li, Dai; Yao, Guizhong

doi:10.1016/j.jad.2019.03.058

Cited by 91 publications

(43 citation statements)

References 42 publications

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“…They were required to watch a series of affective and neutral stimuli under monitoring using EEG, eye tracking, and galvanic skin response; then, three machine learning algorithms including random forests, logistic regression, and SVM were trained to build dichotomous classification model. 53 The results showed that the highest classification f1 score was obtained by logistic regression algorithms (79.6% accuracy, 76.7% precision, 85.2% recall, and 80.7% f1 score). 53 The SVM is a supervised learning method to classify the annotated results with only one solution.…”

Section: Discussionmentioning

confidence: 95%

“…53 The results showed that the highest classification f1 score was obtained by logistic regression algorithms (79.6% accuracy, 76.7% precision, 85.2% recall, and 80.7% f1 score). 53 The SVM is a supervised learning method to classify the annotated results with only one solution. The SVM is considered as one of the most effective classification algorithms available.…”

Section: Discussionmentioning

confidence: 95%

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<p>Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study</p>

Zhao

Feng

2020

NDT

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Purpose Traditional questionnaires assessing the severity of depression are limited and might not be appropriate for military personnel. We intend to explore the diagnostic ability of three machine learning methods for evaluating the depression status of Chinese recruits, using the Chinese version of Beck Depression Inventory-II (BDI-II) as the standard. Patients and Methods Our diagnostic study was carried out in Luoyang City (Henan Province, China; 10/16/2018–12/10/2018) with a sample of 1000 Chinese male recruits selected using cluster convenient sampling. All participants completed the BDI and 3 questionnaires including the data of demographics, military careers and 18 factors. The participants were randomly selected as the training set and the testing at 2:1. The machine learning methods tested for assessing the presence or absence of depression status were neural network (NN), support vector machine (SVM), and decision tree (DT). Results A total of 1000 participants completed the questionnaires, with 223 reporting depression status and 777 not. The highest sensitivity was observed for DT (94.1%), followed by SVM (93.4%) and NN (93.1%). The highest specificity was observed for NN (60.0%), followed by SVM (58.8%) and DT (43.3%). The area under the curve (AUC) of the SVM was the largest (0.862) compared with NN (0.860) and DT (0.734). The regression prediction error and error volatility of the SVM were the smallest. Conclusion The SVM has the smallest prediction error and error volatility, as well as the largest AUC compared with NN and DT for assessing the presence or absence of depression status in Chinese recruits.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 95%

<p>Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study</p>

Zhao

Feng

2020

NDT

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“…Using multimodality MRI, several studies have constructed SVM classifiers for the identification of ASD, Alzheimer's Disease, and schizophrenia [43,[49][50]. Beyond this, some researchers have combined multimodality MRI with other characteristics of these diseases, such as cerebral spinal fluid, electroencephalography, and eye-tracking [51][52]. However, markers selection requires careful consideration, as it has been shown that too much data may not improve the power of the SWM classifier in some instances [53].…”

Section: Discussionmentioning

confidence: 99%

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

Cui

Cao³

et al. 2020

Preprint

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Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The grey matter volumes or ReHo values of the clusters showed significant differences as discriminant features in the SVM classification model. Based on extracted features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, 12 clusters with significant differences were extracted from the data. Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 90% in the test data (p=0.0014). Limitations The sample size was small, and we were unable to eliminate the potential effects of medications. Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

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“…Using multimodality MRI, several studies have constructed SVM classi ers for the identi cation of ASD, Alzheimer's Disease, and schizophrenia [46,[52][53]. Beyond this, some researchers have combined multimodality MRI with other characteristics of these diseases, such as cerebral spinal uid, electroencephalography, and eye-tracking [54][55]. However, markers selection requires careful consideration, as it has been shown that too much data may not improve the power of the SWM classi er in some instances [56].…”

Section: Discussionmentioning

confidence: 99%

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

Cui

Cao³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed for years. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5%, sensitivity of 86.4%, and specificity of 88.9% in the test data (p=0.0022).Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

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Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data

Cited by 91 publications

References 42 publications

<p>Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study</p>

<p>Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study</p>

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

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