ArticleComputational and mathematical methods in medicine2013
Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- EEG Unpredictability in the Resting State of Major Depressive Disorder: A Multidomain EEG Analysis.Brain topography · 2026Article
- Exploring the effects of aerobic and resistance exercise on mood-related symptoms and EEG activity.Frontiers in human neuroscience · 2025Article
- A novel approach for migraine detection using localized component filtering and electroencephalographic spectral asymmetry index.Current journal of neurology · 2024Article
- Advances in Understanding Fractals in Affective and Anxiety Disorders.Advances in neurobiology · 2024Review
- 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
- Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?Advances in neurobiology · 2024Article
- Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.Frontiers in systems neuroscience · 2023Article
- Identification of normal and depression EEG signals in variational mode decomposition domain.Health information science and systems · 2022Article
- A novel EEG-based major depressive disorder detection framework with two-stage feature selection.BMC medical informatics and decision making · 2022Article
- Ensemble Approach for Detection of Depression Using EEG Features.Entropy (Basel, Switzerland) · 2022Article
- Progress in Objective Detection of Depression and Online Monitoring of Patients Based on Physiological Complexity.Frontiers in psychiatry · 2022Article
- Right Frontal Theta: Is It a Response Biomarker for Ketamine's Therapeutic Action in Anxiety Disorders?Frontiers in neuroscience · 2022Article
- Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.Biosensors · 2021Article
- Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review.Journal of medical Internet research · 2020Review
- The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.Cognitive neurodynamics · 2020Article
- Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression.International journal of methods in psychiatric research · 2020Article
- Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review.Entropy (Basel, Switzerland) · 2020Review
- Higuchi's fractal dimension, but not frontal or posterior alpha asymmetry, predicts PID-5 anxiousness more than depressivity.Scientific reports · 2019Article
- The Reason Why rTMS and tDCS Are Efficient in Treatments of Depression.Frontiers in psychology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P < 0.05). SASI provided the true detection rate of 88% in the depressive and 82% in the control group. The calculated HFD values detected a small (3%) increase with depression (P < 0.05). HFD provided the true detection rate of 94% in the depressive group and 76% in the control group. The rate of correct indication in the both groups was 85% using SASI or HFD. Statistically significant variations were not revealed between hemispheres (P > 0.05). The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG.
Indexed as
Identifiers
What OpenQuestion holds
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.