ArticleInternational journal of methods in psychiatric research2020
Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression.
Article in International journal of methods in psychiatric research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Resting-state EEG activity in depression: a systematic review and meta-analysis.NeuroImage. Clinical · 2026Pooled it
- A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.Scientific reports · 2026Article
- Reduced alpha power predicts recurrence risk in major depressive disorder.Neuroimage. Reports · 2026Article
- Rapid eye movement sleep displays distinct fractal dynamics between phasic and tonic states.Neuroscience of consciousness · 2026Article
- Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- Brain network entropy, depression, and quality of life in people with traumatic brain injury and seizure disorders.Epilepsia open · 2024Article
- Article
- Advances in Understanding Fractals in Affective and Anxiety Disorders.Advances in neurobiology · 2024Review
- Article
- Immediate and long-term electrophysiological biomarkers of antidepressant-like behavioral effects after subanesthetic ketamine and medial prefrontal cortex deep brain stimulation treatment.Frontiers in neuroscience · 2024Article
- One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response.Frontiers in psychiatry · 2024Article
- Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG).Advances in neurobiology · 2024Article
- Clinical Sensitivity of Fractal Neurodynamics.Advances in neurobiology · 2024Article
- [Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- EEG alpha band characteristics in patients with a depressive episode within recurrent and bipolar depression.Consortium psychiatricum · 2023Article
- Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population.Scientific reports · 2023Article
- When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures.JMIR mental health · 2023Review
- A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment.Frontiers in psychiatry · 2023Review
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesBiomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions.
methodsResting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex-matched healthy control subjects. Artifact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms.
resultsDepressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro-parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder.
conclusionsComplexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.
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