ArticlePloS one2024
A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- IDMBD: Intelligent Diagnostic Modelling of Bipolar Disorder at its early onset.Scientific reports · 2026Article
- Machine learning approaches to distinguish bipolar disorder from borderline personality disorder: a scoping review.BMC psychiatry · 2026Article
- Understanding mental health discourse on Reddit with transformers and explainability.Scientific reports · 2026Article
- Neural Correlates of Borderline Personality Disorder (BPD) Based on Electroencephalogram (EEG)-A Mechanistic Review.International journal of molecular sciences · 2025Review
- Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification.Life (Basel, Switzerland) · 2025Article
- Curvature estimation techniques for advancing neurodegenerative disease analysis: a systematic review of machine learning and deep learning approaches.American journal of neurodegenerative disease · 2025Review
- Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.American journal of neurodegenerative disease · 2024Article
- Comparative analysis of dimensionality reduction techniques for EEG-based emotional state classification.American journal of neurodegenerative disease · 2024Article
- Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process.American journal of neurodegenerative disease · 2024Review
- Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication.Frontiers in artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.