ArticleJournal of healthcare engineering2020
Frontal Alpha Complexity of Different Severity Depression Patients.
Article in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A feasibility study on inferring connectivity changes in frontal lobes of MDD patients via spectral DCM.Brain informatics · 2026Article
- EEG complexity analysis using enhanced entropy features for depression detection and severity classification.Journal of neuroengineering and rehabilitation · 2026Article
- Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain.Cognitive neurodynamics · 2024Article
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Authors and funding
5 authors.
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Abstract
Depression is a leading cause of disability worldwide, and objective biomarkers are required for future computer-aided diagnosis. This study aims to assess the variation of frontal alpha complexity among different severity depression patients and healthy subjects, therefore to explore the depressed neuronal activity and to suggest valid biomarkers. 69 depression patients (divided into three groups according to the disease severity) and 14 healthy subjects were employed to collect 3-channel resting Electroencephalogram signals. Sample entropy and Lempel-Ziv complexity methods were employed to evaluate the Electroencephalogram complexity among different severity depression groups and healthy group. Kruskal-Wallis rank test and group
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