A note on analyzing schizophrenic EEG with complexity measure

Qin-ye, Tong; Kong, Jun; Xu, Jinghua

doi:10.1016/0960-0779(95)00070-4

Cited by 16 publications

(10 citation statements)

References 1 publication

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“…According to the diagnostic criteria (DSM-IV) of the American Psychiatric Association (1994); International Statistical Classification of Diseases and Health Related Problems (2005), schizophrenic patients show some characteristic symptoms including delusions, hallucinations or disorganized speech. Recently, much attention has been paid to analysis of EEG signals of schizophrenic patients (Barnett & Kirk, 2005;Dierks, Strik, & Maurer, 1995;Elliot Hong et al, 2004;Jin, Ham, & Kim, 2005;Keshavan, David Cashmere, Miewald, & Yeragani, 2004;Kikuchi et al, 2007;Kim et al, 2000;Kirsch, Besthorn, Klein, Rindfleisch, & Olbrich, 2000;Koukkou, Lehmann, Wackermann, Dvorak, & Henggeler, 1993;Light et al, 2006;Boutros et al, 2007;Tong, Kong, & Xu, 1996;Yeragani, Cashmere, Miewald, Tancer, & Keshavan, 2006). In some research Roschke, Fell, & Beckmann, 1995) nonlinear methods have been applied to EEG signals of the two groups of schizophrenic patients and control participants.…”

Section: Introductionmentioning

confidence: 97%

“…Dierks et al (1995) showed that more anterior and superficial equivalent-dipoles were found in the beta-bands for schizophrenic patients compared to healthy control subjects. EEG signals from the temporal-parietal were assessed by computing the correlation dimension during 20 s for schizophrenic and control subjects Tong et al, 1996). The correlation dimension value was significantly higher in first-episode schizophrenics than in controls .…”

Section: Introductionmentioning

confidence: 99%

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An efficient classifier to diagnose of schizophrenia based on the EEG signals

Boostani

Sadatnezhad

Sabeti

2009

Expert Systems with Applications

112

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

An efficient classifier to diagnose of schizophrenia based on the EEG signals

Boostani

Sadatnezhad

Sabeti

2009

Expert Systems with Applications

112

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“…Accompanied by a type-I measure C 1 , C 2 1617 is derived from “a hierarchical approach to complexity of infinite stationary strings22.” By stationary , we see that statistical properties of the strings in consideration are fixed with time or space changing, a precondition for all statistical measures' application.…”

Section: Resultsmentioning

confidence: 99%

“…In this article, C 2 , a statistical measure previously known as of type II1617, is found of somewhat intrinsic C-R adaptability. Further analysis reveals a contradiction between the adaptability and the random UDGM (r-UDGM), i.e.…”

mentioning

confidence: 84%

Unifying Complexity and Information

Deng

2013

Sci Rep

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Complex systems, arising in many contexts in the computer, life, social, and physical sciences, have not shared a generally-accepted complexity measure playing a fundamental role as the Shannon entropy H in statistical mechanics. Superficially-conflicting criteria of complexity measurement, i.e. complexity-randomness (C-R) relations, have given rise to a special measure intrinsically adaptable to more than one criterion. However, deep causes of the conflict and the adaptability are not much clear. Here I trace the root of each representative or adaptable measure to its particular universal data-generating or -regenerating model (UDGM or UDRM). A representative measure for deterministic dynamical systems is found as a counterpart of the H for random process, clearly redefining the boundary of different criteria. And a specific UDRM achieving the intrinsic adaptability enables a general information measure that ultimately solves all major disputes. This work encourages a single framework coving deterministic systems, statistical mechanics and real-world living organisms.

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“…see the reviews of Stam, 2005 andPritchard andDuke, 1992, and reference therein); Information Theory (Tong et al, 1996;Xu et al, 1997;Jeong et al, 2001;Na et al, 2002;Kannathal et al, 2005;Gómez et al, 2006Gómez et al, , 2007Abásolo et al, 2006;Fernández et al, 2007;Figliola et al, 2007;Montani et al, 2007a,b); and time-frequency analysis (Rosso and Mairal, 2002;Rosso et al, 2001Rosso et al, , 2006Rosso, 2007), among other novel techniques have opened up a range of new perspectives for the study of normal and disturbed brain functions using background EEG.…”

Section: Introductionmentioning

confidence: 98%

Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity

Rosso

Mendes

Rostas

et al. 2009

Journal of Neuroscience Methods

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Background electroencephalography (EEG), recorded with scalp electrodes, in children with childhood absence epilepsy (CAE) and control individuals has been analyzed. We considered 5 CAE patients, all right-handed females and aged 6-8 years. The 15 control individuals had the same characteristics of the CAE ones, but presented a normal EEG. The EEG was obtained using bipolar connections from a standard 10-20 electrode placement (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2). Recordings were undertaken in the resting state with eyes closed. EEG hallmarks of absence seizure activity are widely accepted, but there is a recognition that the bulk of interictal EEG in CAE appears normal to visual inspection. The functional activity between electrodes was evaluated using a wavelet decomposition in conjunction with the Wootters distance. Then, pairs of electrodes with differentiated behavior between CAE and controls were identified using a test statistic-based feature selection technique. This approach identified clear differences between CAE and healthy control background EEG in the frontocentral electrodes, as measured by Principal Component Analysis. The findings of this pilot study can have strong implications in future clinical practice.

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A note on analyzing schizophrenic EEG with complexity measure

Cited by 16 publications

References 1 publication

An efficient classifier to diagnose of schizophrenia based on the EEG signals

An efficient classifier to diagnose of schizophrenia based on the EEG signals

Unifying Complexity and Information

Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity

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