Linear and nonlinear analysis of electroencephalogram of the coma

Chan, Hing‐Yuen; Lin, Ming-An; Fang, Shih-Chin

doi:10.1109/iembs.2004.1403227

Cited by 9 publications

(7 citation statements)

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“…k can be achieved directly through (9) by the universal algorithm of trial and error, and then the upper limit b'(S) of c(S) can be determined through (10). Formula (10) is the original formula to determine the upper limit of c(S), so the b'(S) directly determined through (10) approaches the true value more closely than the b(S) approximately determined through (4). It is also not necessary to be derived from (1) or (4), which avoids the requirement of the critical value of n or the assumption of a sufficient sequence length.…”

Section: The Characteristics Of the Universal Algorithm Of Trial And mentioning

confidence: 96%

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Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series

Liu

Tang

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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The Lempel-Ziv algorithm is not fully suitable for measuring the complexity of short series. When the Lempel-Ziv algorithm is applied, the assumption that the sequence must be long enough is always ignored: the series length is usually too large, and the equi-probability assumption also is not considered in some papers. Based on these facts, the universal algorithm of trial and error is presented when the conditions of different sequence lengths and different symbol probabilities are comprehensively considered. The facts that the overvaluation would occur in measuring normalized complexity of short sequences with the original Lempel-Ziv algorithm and obtained results through improved LempelZiv algorithm more in accord with the reality are testified by virtue of tests of ultra-short series, Logistic equation of different state, observed traffic flow.

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Section: The Characteristics Of the Universal Algorithm Of Trial And mentioning

confidence: 96%

“…But the non-equiprobability of different symbols in sequences is not considered in references [3], [4], [8]. Thus, neglecting the equiprobability assumption will contribute to an aspect of inaccurate applications of the Lempel-Ziv algorithm.…”

Section: Neglecting the Equiprobability Assumptionmentioning

confidence: 98%

Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series

Liu

Tang

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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“…However, while approaches based on spectral analysis are commonplace, this form of analysis only captures sinusoidal harmonic structure in the underlying signal. Other forms of spatiotemporal time series analysis, such as measures of signal entropy and complexity, are available that may complement and augment spectral methods [ 28 ], and have been applied to EEG data from limited cohorts of patients with DLOC [ 29 , 30 ]. We investigated these and other temporal markers to determine their potential for classifying focal and diffuse DLOC.…”

Section: Introductionmentioning

confidence: 99%

EEG dynamical correlates of focal and diffuse causes of coma

et al. 2017

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BackgroundRapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals.MethodsWe retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases.ResultsUsing cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%.ConclusionsOur findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12883-017-0977-0) contains supplementary material, which is available to authorized users.

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“…La entropía espectral ha sido empleada para caracterizar la actividad cerebral durante el sueño (Fell et al 1996) y para monitorizar la profundidad anestésica . Además, se ha utilizado para detectar diferencias en registros EEG de enfermos epilépticos (Capurro et al 1998), pacientes que han sufrido isquemia cerebral (Bezerianos et al 2003) y sujetos en coma (Chan et al 2004). También ha servido para estudiar los cambios producidos por estímulos auditivos, visuales y bimodales (Yordanova et al 2002) y por la realización de tareas mentales (Inouye et al 1991).…”

Section: Entropía Espectral De Shannonunclassified

Análisis no lineal de registros magnetoencefalográficos para la ayuda en el diagnóstico de la enfermedad de Alzheimer

Peña¹

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In this Doctoral Thesis, we have examined the magnetoencephalography (MEG) background activity in 36 Alzheimer's disease (AD) patients and 26 elderly control subjects by means of non-linear methods. Our goal was to test the hypothesis that brain activity recorded in MEG signals is different in AD patients than in control subjects. AD is the most frequent form of dementia in western countries. This brain disorder affects 1% of the population aged 60-64 years, but the prevalence increases exponentially with age, so around 30% of people over 85 years suffer from this disease. Additionally, due to the fact that life expectancy has increased significantly in western countries during the last decades, it is expected that the number of people with dementia will increase to 81 million in 2040. As a definitive diagnosis is only possible by necropsy, the differential diagnosis with other types of dementia and with major depression is becoming more important. This differential diagnosis includes medical history, physical and neurological evaluation, laboratory studies and neuroimaging techniques. Mental status tests are also used to assess the severity of cognitive deficit. Diagnostic tools and criteria make possible for physicians to pursue a positive clinical diagnosis with an accuracy between 80 and 90%. Hence, new approaches are needed to improve AD detection. Nowadays, MEG recordings are not used in AD clinical diagnosis. Nevertheless, several studies have demonstrated that the analysis of brain signals could help physicians in the diagnosis of this dementia. MEG, as electroencephalography (EEG), is a non-invasive technique that allows to record the electromagnetic fields produced by brain activity with good temporal resolution. The vi use of MEG recordings to study the background brain activity offers some advantages over EEG. MEG provides reference-free recordings, which are not distorted by the resistive properties of the skull. Additionally, MEG offers higher spatial resolution than conventional EEG. On the other hand, the magnetic signals generated by the human brain are extremely weak. Thus, superconductive devices are necessary to detect them and MEGs must be recorded in a magnetically shielded room. Therefore, MEG is characterized by limited availability and high equipment cost. Non-linearity is present in many physiological signals. For a neuronal network such as the brain, non-linearity is introduced even at the cellular level, since the dynamical behaviour of individual neurons is governed by threshold and saturation phenomena. Moreover, non-linear methods have demonstrated their usefulness for the analysis of brain recordings in AD. For these reasons, we have examined the MEG background activity in AD patients and control subjects with several non-linear techniques: Lempel-Ziv complexity, Higuchi's fractal dimension, Maragos and Sun's fractal dimension, Shannon spectral entropy, approximate entropy, sample entropy, multiscale entropy, non-linear forecasting, auto mutual information, detrended fluctuation an...

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Linear and nonlinear analysis of electroencephalogram of the coma

Cited by 9 publications

References 4 publications

Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series

Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series

EEG dynamical correlates of focal and diffuse causes of coma

Análisis no lineal de registros magnetoencefalográficos para la ayuda en el diagnóstico de la enfermedad de Alzheimer

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