Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data

Ravan, Maryam; Noroozi, Amin; Sanchez, M. Margarette; Borden, Linnea; Alam, Nur-A; Flor-Henry, P.; Hasey, Gary

doi:10.1016/j.clinph.2022.11.014

Cited by 9 publications

(7 citation statements)

References 56 publications

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“…Ravan et al . [ 47 ] introduced a multi-step preprocessing method and proposed an ML algorithm using extracted symbolic transfer entropy features to distinguish MDD from BD patients. They employed a training dataset of resting state EEG from 71 MDD and 71 BD patients.…”

Section: Resultsmentioning

confidence: 99%

How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

Song,

Lee,

Kim

et al. 2024

Clin Psychopharmacol Neurosci

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Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

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Section: Resultsmentioning

confidence: 99%

How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

Song,

Lee,

Kim

et al. 2024

Clin Psychopharmacol Neurosci

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“…Further, recent studies find meaningful results on the other qEEG measures such as synchronization, 19 functional cortical networks, 18,20 cordance, 21 and coherence, 21 or they report qEEG activity discrimination based on machine learning techniques. 26,27 These studies revealed that both patients with MDD and BD had abnormalities in functional cortical networks; however, they show comparable distinctions which machine learning techniques can detect.…”

Section: Discussionmentioning

confidence: 98%

“…The extracted features differentiating the patients were mainly in the frontal and parietal cortex. 27…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…The total YMRS score varies between 0 and 60. YMRS scores are categorized as follows: remission (0-12), minimal symptoms (13)(14)(15)(16)(17)(18)(19), mild symptoms (20)(21)(22)(23)(24)(25), moderate symptoms (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37), and severe symptoms (38-60). 40 qEEG Recording Procedure A 19-channel (FP1, F7, T3, T5, F3, C3, P3, O1, Fz, Cz, Pz, F4, C4, P4, O2, FP2, F8, T4, and T6) electro-cap was used to record resting-state qEEG recordings.…”

Section: Clinical Measuresmentioning

confidence: 99%

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Predictive Value of qEEG in Manic Switch of Depressed Patients

Arıkan¹,

İlhan²,

Ozulucan³

et al. 2023

Clin EEG Neurosci

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Backgrounds More than half of the patients with bipolar disorder (BD) had depressive episodes at the onset of BD. Despite some suggested clinical predictors, there are no certain criteria for predicting which unipolar depression patient switch to manic episodes during the treatment course. Electrophysiological markers can address this issue. Methods Pretreatment quantitative electroencephalography (qEEG) records of patients diagnosed with major depressive disorder (MDD) or BD at the first visit were included in the study. Patients with MDD were also grouped with manic switch (MS) or MDD based on the diagnosis of later visits. The qEEG spectral power was analyzed across 3 groups, that is, MS, MDD, and BD. Results Compared to patients whose diagnosis did not change, patients with MS had accelerated high-frequency activities predominantly in the left hemisphere (central-parietal-occipital regions). In contrast, they showed increased slow wave activity predominantly in the right hemisphere (parietal-occipital regions). Conclusion It can be concluded that searching for electrophysiological markers, which have distinct advantages of repeatability, noninvasiveness, and cost-effectiveness, can facilitate the prediction of the MS.

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“… 49 , 50 , 51 , 52 , 53 In particular, TE has also been employed to analyse electroencephalogram signals in individuals with mood disorders. 54 , 55 , 56 …”

Section: Introductionmentioning

confidence: 99%

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data

Song,

Jeong,

de los Reyes

et al. 2024

eBioMedicine

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Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data

Cited by 9 publications

References 56 publications

How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

Predictive Value of qEEG in Manic Switch of Depressed Patients

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data

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