On arousal from sleep: time-frequency analysis

Méndez, Martín O.; Bianchi, Anna Maria; Montano, Nicola; Patruno, Vincenzo; Gil, Eduardo; Mantaras, C.; Aiolfi, S; Cerutti, S.

doi:10.1007/s11517-008-0309-z

Cited by 32 publications

(23 citation statements)

References 48 publications

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“…The EEG has proved to be useful in the characterization of different physiological or pathological conditions, such as, for example, sleep [29] or epilepsy [11], [18]. Particularly, the EEG has been used as a tool for diagnosing dementias for several decades.…”

Section: Introductionmentioning

confidence: 99%

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

127

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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: Introductionmentioning

confidence: 99%

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

127

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“…We can observe how the spectral components of the heart rate variations are correlated with the episode. During the episode, a frequency component around 0.04 Hz appears, thus indicating an increase in the sympathetic drive (Mendez et al 2008).…”

Section: (B ) Sleep Intended As a Multiorgan Manifestationmentioning

confidence: 98%

Multiscale, multiorgan and multivariate complexity analyses of cardiovascular regulation

Cerutti

Hoyer

Voss

2009

Phil. Trans. R. Soc. A.

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Cardiovascular system complexity is confirmed by both its generally variegated structure of physiological modelling and the richness of information detectable from processing of the signals involved in it, with strong linear and nonlinear interactions with other biological systems. In particular, this behaviour may be accordingly described by means of what we call MMM paradigm (i.e. multiscale, multiorgan and multivariate). Such an approach to the cardiovascular system emphasizes where the genesis of its complexity is potentially allocated and how it is possible to detect information from it. No doubt that processing signals from multi-leads of the same system (multivariate), from the interaction of different physiological systems (multiorgan) and integrating all this information across multiple scales (from genes, to proteins, molecules, cells, up to the whole organ) could really provide us with a more complete look at the overall phenomenon of cardiovascular system complexity, with respect to the one which is obtainable from its single constituent parts. In this paper, some examples of approaches are discussed for investigating the cardiovascular system in different time and spatial scales, in studying a different organ involvement (such as sleep, depression and multiple organ dysfunction) and in using a multivariate approach via various linear and nonlinear methods for cardiovascular risk stratification and pathology assessment.

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“…Patients with OSA suffer from hundreds of episodes of such events every night [1,13,17]. Consequences of these events are O 2 de-saturation [17], increase in heart rate and blood pressure [2,18], and electroencephalograph-arousal (EEGA) [7,9]. PLMS is characterized by repetitive movements of the legs during sleep [13].…”

Section: Daytime Sleepiness and Sleep Fragmentationmentioning

confidence: 99%

A state transition-based method for quantifying EEG sleep fragmentation

Swarnkar

Abeyratne

Hukins

et al. 2009

Med Biol Eng Comput

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Sleep fragmentation is the predominant factor causing excessive daytime sleepiness in diseases such as sleep apnea and periodic leg movement syndrome. The reference standard for quantifying sleep fragmentation is the arousal index (ArI), which is defined as the average number of arousals per hour of sleep. Arousal scoring is tedious and subjective resulting in considerable inter-and intra-rater variability. Moreover, ArI is only weakly correlated with other indicators of sleep fragmentation such as the total sleep time (TST) and the sleep efficiency (SE). This introduces consistency problems, making the ArI difficult to interpret in practice. In this article, we address these issues by proposing a novel measure of sleep fragmentation termed the weighted-transition sleep fragmentation index (v). This new measure is derived by capturing the different sleep states transitions and assigning weights to them. A significant correlation was found between v and all other indices of sleep fragmentation (r = 0.72, r = 0.0001, r = -0.59, r = 0.001, r = -0.72, r = 0.0001, respectively, for ArI, TST and SE. These results suggest that v is an accurate and useful tool for clinical practice.

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On arousal from sleep: time-frequency analysis

Cited by 32 publications

References 48 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

Multiscale, multiorgan and multivariate complexity analyses of cardiovascular regulation

A state transition-based method for quantifying EEG sleep fragmentation

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