A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data

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

doi:10.1109/embc48229.2022.9871453

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…[ 47 ] and Sanchez et al . [ 56 ] used the minimum redundancy maximum relevance (mRMR) method. mRMR uses the correlation between features, and it selects the features with the highest correlation (relevance) and lowest correlation (redundancy) between the classes [ 57 ].…”

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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show abstract

“…ajor Depressive Disorder (MDD) is a prevalent psychiatric disorder with a global impact [1], which severely impairs the social function and quality of life of patients [2]. However, the clinical heterogeneity and neurobiological complexity of MDD [3] pose challenges for its the study of the neurogenesis mechanism and early prediction.…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network

Yuan,

Chen,

Ding

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objectives: Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. Methods: The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. Results: The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated (p<0.05) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks (p<0.05 or p<0.01). Conclusions and Significance: These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.

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“…Secondly, the proposed method takes advantage of symbolic transfer entropy (STE) to extract connectivity features between sensors gathering data from different body parts such as eyes and hands. Although STE has been applied to sensor data of the same type, for example, EEG data, for other applications [18,19], this is the first study applying STE to sensor data of different types for eSports players skill evaluation, thereby incorporating the harmony between body parts as features in the classification. Thirdly, we propose a novel feature selection procedure comprising a recently developed method called consensus nested cross-validation (CN-CV) [20] and the minimum redundancy maximum relevance (mRMR) method [21].…”

Section: Introductionmentioning

confidence: 99%

An efficient machine learning approach for extracting eSports players’ distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross-validation

Noroozi,

Hasan,

Ravan

et al. 2024

Int J Data Sci Anal

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Discovering features that set elite players apart is of great significance for eSports coaches as it enables them to arrange a more effective training program focused on improving those features. Moreover, finding such features results in a better evaluation of eSports players' skills, which, besides coaches, is of interest for game developers to design games automatically adaptable to the players' expertise. Sensor data combined with machine learning have already proved effective in classifying eSports players. However, the existing methods do not provide sufficient information about features that distinguish high-skilled players. In this paper, we propose an efficient method to find these features and then use them to classify players' skill levels. We first apply a time window to extract the players' sensor data, including heart rate, hand activities, etc., before and after game events in the League of Legends game. We use the extracted segments and symbolic transfer entropy to calculate connectivity features between sensors. The most relevant features are then selected using the newly developed consensus nested cross validation method. These features, representing the harmony between body parts, are finally used to find the optimum window size and classify players' skills. The classification results demonstrate a significant improvement by achieving 90.1% accuracy. Also, connectivity features between players' gaze positions and keyboard, mouse, and hand activities were the most distinguishing features in classifying players' skills. The proposed method in this paper can be similarly applied to sportspeople's data and potentially revolutionize the training programs in both eSports and sports industries.

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A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data

Cited by 6 publications

References 31 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

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network

An efficient machine learning approach for extracting eSports players’ distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross-validation

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