Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

Abásolo, Daniel; Hornero, Roberto; Espino, Pedro; Poza, Jesús; Sánchez, Clara I.; Rosa, R. de la

doi:10.1016/j.clinph.2005.04.001

Cited by 210 publications

(175 citation statements)

References 38 publications

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“…A possible explanation for this result might be that the inspection of different time scales provides an advantage in comparison to the use of other non-linear measures based on one time scale only when analysing physiological signals [9], [20], [31]. Our results are consistent with previous studies showing changes in the EEG of AD patients in comparison to age-matched control subjects with different non-linear metrics [1], [24], [25], [39], [44]. The abnormalities in the AD patients' EEG could be explained by a change of the dynamics in the brain.…”

Section: Discussionsupporting

confidence: 91%

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

Abásolo

Escudero

Hornero

et al. 2008

Med Biol Eng Comput

117

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We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two non-linear methods: Approximate Entropy (ApEn) and Auto Mutual Information (AMI). ApEn quantifies regularity in data, while AMI detects linear and non-linear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The EEG AMI decreased more slowly with time delays in patients than in controls, with significant differences at electrodes T5, T6, O1, O2, P3 and P4 (p < 0.01). The strong correlation between results from both methods shows that the AMI rate of decrease can be used to estimate the regularity in time series. Our work suggests that non-linear EEG analysis may contribute to increase the insight into brain dysfunction in AD, especially when different time scales are inspected, as is the case with AMI.

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Section: Discussionsupporting

confidence: 91%

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

Abásolo

Escudero

Hornero

et al. 2008

Med Biol Eng Comput

117

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“…These results are in agreement with previous research works that have applied non-linear methods to estimate the regularity of the AD patients' brain activity. 1,2,12,17 ApEn values were significantly lower in the EEG of AD patients at electrodes P3 and P4, 1 whereas statistically significant differences were found at P3, P4, O1 and O2 using SampEn. 2 Our results also showed that AD patients have lower LZC values than controls.…”

Section: Discussionmentioning

confidence: 73%

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

et al. 2009

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“…As r is normalized by the standard deviation, ApEn is amplitude scale independent. These values have proven to perform well in many cases (Abásolo et al, 2005;Pincus and Keefe, 1992;Kaplan et al, 1991). Results for each register and each class are shown in Table 1.…”

Section: Resultsmentioning

confidence: 95%

“…It has been used to analyse information provided by the electroencephalogram related to the Alzheimer's disease (Abásolo et al, 2005), or study implications of heart rate dynamics (Ryan et al, 1994), among others. For temperature registers, our results confirm the usefulness of this measure for distinguishing the two classes ( t-test, p = 0.0011, and fMW test, p = 0.0028).…”

Section: Discussionmentioning

confidence: 99%

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Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy

et al. 2007

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Body temperature is a classical diagnostic tool for a number of diseases. However, it is usually employed as a plain binary classification function (febrile or not febrile), and therefore its diagnostic power has not been fully developed. In this paper we describe how body temperature regularity can be used for diagnosis. Our proposed methodology is based on obtaining accurate long-term temperature recordings at high sampling frequencies and analyzing the temperature signal using a regularity metric (approximate entropy). In this study we assessed our methodology using temperature registers acquired from patients with multiple organ failure admitted to an intensive care unit. Our results indicate there is a correlation between the patient's condition and the regularity of the body temperature. This finding enabled us to design a classifier for two outcomes (survival or death) and test it on a dataset including 36 subjects. The classifier achieved an accuracy of 72%.

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Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

Cited by 210 publications

References 38 publications

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy

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