Power and coherent oscillations distinguish REM sleep, stage 1 and wakefulness

Corsi-Cabrera, María; Muñoz‐Torres, Zeidy; Río-Portilla, Yolanda del; Guevara, Miguel Ángel

doi:10.1016/j.ijpsycho.2005.05.004

Cited by 51 publications

(32 citation statements)

References 36 publications

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“…The same behavior is observed in [7]. Furthermore, as occurred in [7] and [20], the S1 stage is easily classified as REM and its adjacent stages, i.e. awake and S2.…”

Section: Sleep Stages Classification Using Spectral Based Statisticalsupporting

confidence: 61%

“…Sigma oscillations occur mainly during S2 stage [19]. Corsi-Cabrera et al [20] claim that the beta rhythm (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) is primarily associated to the awake stage. Finally, the gamma rhythm (30-80 Hz) is related to the working memory and attention [21] and, thus, arises mainly during the awake stage.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Braun

Kozakevicius

Silveira

et al. 2018

RITA

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Abstract:In the pursuit of portable, efficient and effective sleep staging systems, researchers have been testing a massive number of combinations of EEG features and classifiers. State of the art sleep classification ensembles achieve accuracy in the order of 90%. However, there is presently no consensus regarding the best set of features for identifying sleep stages with a single EEG channel, leading researchers to modify the feature selection according to the number of classification stages. This paper introduces a reduced set of frequency-domain features capable of yielding high classification accuracy (90.9%, 91.8%, 92.4%, 94.3% and 97.1%) for all 6-to 2-state sleep stages. The proposed system uses fast Fourier transform (FFT) to convert data from Pz-Oz EEG channel into the frequency domain. Afterwards, eight statistical features are extracted from specific frequency ranges associated to brain rhythms, feeding a random forest classifier. Keywords: sleep stage classification -random forest -FFT -statistical momentsResumo: Na busca por sistemas portáveis, eficientes e efetivos para classificação de sono, pesquisadores têm testado um grande número de combinações de atributos de sinais EEG e classificadores. O estado da arte dos aplicativos de classificação para estágios de sono atinge taxas de acerto na ordem de 90%. Contudo, não existe um consenso em relação ao melhor grupo de atributos para identificar estágios de sono em sinais EEG de umúnico canal, levando pesquisadores a modificar a seleção dos atributos de acordo com o número de estágios a serem classificados. Este trabalho apresenta um conjunto reduzido de atributos, capaz de produzir uma classificação com taxas elevadas de acerto (90.9%, 91.8%, 92.4%, 94.3% and 97.1%) para todos os agrupamentos de estágios de sono (de 6 a 2 estágios). O sistema proposto utiliza a transformada rápida de Fourier (FFT) para converter os dados do canal de EEG Pz-Oz para o domínio da frequência. Posteriormente, oito atributos estatísticos são extraídos de faixas de frequência específicas associadas a ritmos cerebrais, alimentando um classificador do tipo floresta aleatória. Palavras-Chave: classificação de estágios de sono -floresta aleatória -FFT -momentos estatísticos

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“…The same behavior is observed in [7]. Furthermore, as occurred in [7] and [20], the S1 stage is easily classified as REM and its adjacent stages, i.e. awake and S2.…”

Section: Sleep Stages Classification Using Spectral Based Statisticalsupporting

confidence: 61%

Section: Related Workmentioning

confidence: 99%

Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Braun

Kozakevicius

Silveira

et al. 2018

RITA

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“…From Table III, it can be seen that most N1 epochs are misclassified as Wake, REM and N2 while substantial number of Wake epochs are also falsely classified as N1 and REM. This is not unexpected since certain spectral similarities between these three stages are well documented [21], [22].…”

Section: Discussionmentioning

confidence: 52%

Automatic sleep staging using state machine-controlled decision trees

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.

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“…We demonstrated for the first time that stage 1 represent very similar functionality with stage 4 even though the former is characterized by alpha (2-7 Hz) and the latter by delta (\2 Hz) activity respectively. On the other hand, REM and stage 1 differ a lot in terms of functional interactions (Corsi-Cabrera et al 2006). These results contribute to the understanding of brain functionality changes during REM sleep and stage 1 which will help to intrude to their physiology and may be relevant for understanding disorders in falling asleep.…”

Section: Discussionmentioning

confidence: 81%

Characterizing Dynamic Functional Connectivity Across Sleep Stages from EEG

et al. 2008

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Following a nonlinear dynamics approach, we investigated the emergence of functional clusters which are related with spontaneous brain activity during sleep. Based on multichannel EEG traces from 10 healthy subjects, we compared the functional connectivity across different sleep stages. Our exploration commences with the conjecture of a small-world patterning, present in the scalp topography of the measured electrical activity. The existence of such a communication pattern is first confirmed for our data and then precisely determined by means of two distinct measures of non-linear interdependence between time-series. A graph encapsulating the small-world network structure along with the relative interdependence strength is formed for each sleep stage and subsequently fed to a suitable clustering procedure. Finally the delineated graph components are comparatively presented for all stages revealing novel attributes of sleep architecture. Our results suggest a pivotal role for the functional coupling during the different stages and indicate interesting dynamic characteristics like its variable hemispheric asymmetry and the isolation between anterior and posterior cortical areas during REM.

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Power and coherent oscillations distinguish REM sleep, stage 1 and wakefulness

Cited by 51 publications

References 36 publications

Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Automatic sleep staging using state machine-controlled decision trees

Characterizing Dynamic Functional Connectivity Across Sleep Stages from EEG

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