Fractality analysis of frontal brain in major depressive disorder

Ahmadlou, Mehran; Adeli, Hojjat; Adeli, Amir

doi:10.1016/j.ijpsycho.2012.05.001

Cited by 244 publications

(148 citation statements)

References 60 publications

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“…The goal is to help physicians indicate different sleep stages and the occurrence of respiratory, cardiac and muscular events in the sleep scoring process [27]. Combinations of wavelet signal processing technique, chaos theory/nonlinear science and neural network pattern recognition techniques have been reported in recent years for the EEG-based diagnosis of varying disorders such as epilepsy [28,29,30,31,32,33], Alzheimer's disease [34,35], attention deficit hyperactivity disorder [36,37], autism spectrum disorder [38,39,40], major depressive disorder [29] and alcoholism [41]. …”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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Background: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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“…A few studies have applied nonlinear methods namely FD [66], wavelet-based energy [43] and entropies [44] to extract EEG signal features and artificial neural networks (ANN) [43] to classify EEG signal features into depression and normal behaviour. Ahmadlou et al [66] investigate EEGs obtained from patients with Major Depressive Disorder (MDD) using the wavelet-chaos methodology developed by Adeli and associates earlier for EEG-based diagnosis of epilepsy [67] and Higuchi's and Katz's fractal dimension (HFD and KFD) [68,69] as measures of complexity and nonlinearity. They compared frontal lobes (left and right) HFDs and KFDs in EEG full-band and various sub-bands of MDD and control groups with the goal of discovering relevant differences in terms of FDs between the two groups.…”

Section: Nonlinear Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis of Depression Using EEG Signals

Acharya

Sudarshan

Adeli³

et al. 2015

Eur Neurol

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The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

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“…Synchronization measurements are classified in two general categories: linear methods and nonlinear methods. such as synchronization likelihood (introduced by Stam and van Dijk (2002), fuzzy synchronization likelihood (introduced by Ahmadlou and Adeli (2011b), and visibility graph similarity (introduced by Ahmadlou and Adeli (2012)) for diagnosis of attention deficit/hyperactivity disorder (ADHD) Adeli, 2010b, 2012;Ahmadlou and Adeli, 2011 a,b;Ahmadlou et al, 2012a), Autism Spectrum Disorder (ASD) (Ahmadlou et al, 2012b), and Major Depressive Disorder (MDD) (Ahmadlou et al, 2012c). A large number of sequential sample times is needed for nonlinear synchronization measurements (usually more than 10,000 sample times).…”

Section: Functional Connectivitymentioning

confidence: 99%

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task

Ahmadlou

Adeli

Bajo

et al. 2014

Clinical Neurophysiology

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HIGHLIGHTS• We use magneto-encephalograms in patients with mild cognitive impairment (MCI).• We investigate complexity of functional connectivity network of MCI patients using two different measures: Graph Index Complexity and Efficiency Complexity.• Efficiency Complexity is superior to Graph Index Complexity and its value at theta band can be used for diagnosis of MCI. ABSTRACT Objectives:The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task. Methods: MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks' complexities are measured by Graph Index Complexity (C r ) and Efficiency Complexity (Q). Results:The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere. Conclusions: C e would be a better measurement for showing the global differences between normal and MCI brains compared with C r . Significance: The high accuracy of the classification shows C e at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MCI.

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Fractality analysis of frontal brain in major depressive disorder

Cited by 244 publications

References 60 publications

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Computer-Aided Diagnosis of Depression Using EEG Signals

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task

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