Machine learning approaches for diagnosing depression using EEG: A review

Liu, Yuan; Pu, Changqin; Shan, Xia; Deng, Dingyu; Wang, Xing; Li, Mengqian

doi:10.1515/tnsci-2022-0234

Cited by 19 publications

(5 citation statements)

References 53 publications

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“…Depression is a neurological syndrome with three main symptoms, which refer to slow thinking, low mood, and cognitive impairment [ 1 , 2 , 3 ]. So far, depression has been one of the most common mental illnesses and has evolved into a global problem [ 4 ]. Most humans can be victims of depression, ranging from children to the elderly [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Zhong

Wang

et al. 2023

Brain Sciences

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Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Zhong

Wang

et al. 2023

Brain Sciences

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“…The increasing number of studies confirms the high accuracy of the detection of depression from ECG signals with ML [27]. Among the included studies, the highest classification accuracy was up to 99.5% [28], which offers the potential for screening and prevention of early clinical depression.…”

Section: Detection Of Depression and Ptsdmentioning

confidence: 66%

Recognition of Mental Disorders From Physiological Signals Analysis

Sukholeister¹,

Наконечный²

2022

MEM

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The rapid advances in machine learning (ML) and information fusion have made it possible to use machines/computers with the ability of understanding, recognition, and analysis of human emotion, mood and stress, and related mental diseases. The recognition methods based on physiological modalities are the most performant. Wearable technologies enable non-invasive long-term data gathering and analysis. The number of mental health issues are correlated with emotional states and can be possibly detected by similar methods to general emotion recognition. The scientific interest in the recognition of mental disorders is growing, and most of the available studies are uni-modal based on either ECG or EEG sensor data, while some recent studies also utilize multiple modalities and sensor fusion.

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“…Previous research applying machine learning to EEG data has often fallen short due to the lack of appropriate model validations within the studies [35]. For example, many studies have overestimated the performance of the created models by evaluating their performance without preparing test data with over-trained models or by showcasing only the coincidental best accuracy obtained with the best combination of datasets [18].…”

Section: Discussionmentioning

confidence: 99%

Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices

Noda,

Sakaue,

Wada

et al. 2024

JPM

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Depression is the disorder with the greatest socioeconomic burdens. Its diagnosis is still based on an operational diagnosis derived from symptoms, and no objective diagnostic indicators exist. Thus, the present study aimed to develop an artificial intelligence (AI) model to aid in the diagnosis of depression from electroencephalography (EEG) data by applying machine learning to resting-state EEG and transcranial magnetic stimulation (TMS)-evoked EEG acquired from patients with depression and healthy controls. Resting-state EEG and single-pulse TMS-EEG were acquired from 60 patients and 60 healthy controls. Power spectrum analysis, phase synchronization analysis, and phase-amplitude coupling analysis were conducted on EEG data to extract feature candidates to apply different types of machine learning algorithms. Furthermore, to address the limitation of the sample size, dimensionality reduction was performed in a manner to increase the quality of information by featuring robust neurophysiological metrics that showed significant differences between the two groups. Then, nine different machine learning models were applied to the data. For the EEG data, we created models combining four modalities, including (1) resting-state EEG, (2) pre-stimulus TMS-EEG, (3) post-stimulus TMS-EEG, and (4) differences between pre- and post-stimulus TMS-EEG, and evaluated their performance. We found that the best estimation performance (a mean area under the curve of 0.922) was obtained using receiver operating characteristic curve analysis when linear discriminant analysis (LDA) was applied to the combination of the four feature sets. This study showed that by using TMS-EEG neurophysiological indices as features, it is possible to develop a depression decision-support AI algorithm that exhibits high discrimination accuracy.

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Machine learning approaches for diagnosing depression using EEG: A review

Cited by 19 publications

References 53 publications

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Recognition of Mental Disorders From Physiological Signals Analysis

Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices

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