Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea

Carvalho, Diego Z.; Gerhardt, Günther J.L.; Dellagustin, Guilherme; Santa-Helena, Emerson L. de; Lemke, Ney; Segal, Alan Z.; Schönwald, Suzana Veiga

doi:10.1016/j.clinph.2013.07.005

Cited by 56 publications

(59 citation statements)

References 59 publications

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“…Correspondingly, comparatively little is known about the neuromechanisms of OSA-induced brain injury, likely exerted via repeated arousals disturbing different stages of sleep [9]. Furthermore, several recent clinical studies suggest disturbed spatiotemporal evolution of sleep spindles in patients with OSA during the night [23,85]. Furthermore, several recent clinical studies suggest disturbed spatiotemporal evolution of sleep spindles in patients with OSA during the night [23,85].…”

Section: Obstructive Sleep Apnea and The Disturbance Of Sleep Structurementioning

confidence: 99%

The impact of sleep and hypoxia on the brain

Rosenzweig

Williams

Morrell

2014

Current Opinion in Pulmonary Medicine

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Section: Obstructive Sleep Apnea and The Disturbance Of Sleep Structurementioning

confidence: 99%

The impact of sleep and hypoxia on the brain

Rosenzweig

Williams

Morrell

2014

Current Opinion in Pulmonary Medicine

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“…These results correlate well with theories of brain plasticity. Chronic problems, such as apnea, Parkinson's disease, epileptic encephalopathy, and others eventually impact neural mechanisms through final common pathways that may translate into lower amplitudes for short time EEG transients like spindles [7], [14]- [17]. This is a preliminary work where it was possible to perform systematic MP decomposition of sleep EEG signals pertaining to a representative sample of apnea patients, thereby finding a dictionary size more appropriate for this type of study.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the signal is decomposed into a sum of waveforms, prototypes or atoms with different weights or coefficients. For further methodology description see [3], [5]- [7], [9]- [11].…”

Section: B Matching Pursuitmentioning

confidence: 99%

“…The amount of results obtained in a week of GRID processing was comparable to more than a decade of processing on conventional PCs. Our dataset was processed during the winter of 2010 and has been analyzed since that time, advancing knowledge on the sleep EEG frequency-distribution problem in OSAS patients [5]- [7]. The aim of this work is to present the GRID/UNESP computing system helping to understand a real clinical EEG problem.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of EEG Sleep Spindle Parameters from Apnea Patients Using Massive Computing and Decision Tree

et al. 2014

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In this study, Matching Pursuit (MP) procedure is applied to the detection and analysis of EEG sleep spindles in patients evaluated for suspected OSAS. Elements having the frequency of EEG sleep spindles are selected from different dictionary sizes, with and without a frequency modulation function (chirp) for signal description. This procedure was done with high computational cost in order to find best parameters for real EEG data description. At the end we used the atom parameters as input for a decision tree-based classifier, making possible to obtain a classification according to apnea-hypopnea index group and allowing to see how atom parameters such as frequency and amplitude are affected by the presence of sleep apnea.

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“…Sleep spindles reflect a heritable set of traits which is implicated in both sleep regulation and normal cognitive functioning [38]. Recent studies have linked spindle density (number of spindles per minute), duration and amplitude of spindles, and peak frequency of spindles to memory consolidation during sleep [31,15], cognition in schizophrenia patients [38,66], brain dysfunction in obstructive sleep apnea [14] and biomarkers for Alzheimer's disease [69]. As a result, understanding the characteristics of sleep spindles is a key in studying their relation to several neuropsychiatric diseases.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Sleep Spindle Detection using Sparse Low-Rank Optimization

Parekh

Selesnick

Osorio

et al. 2017

Preprint

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Background: We propose a multichannel spindle detection method that detects global and local spindle activity across all channels of scalp EEG in a single run. New Method: Using a non-linear signal model, which assumes the multichannel EEG to be a sum of a transient component and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated multichannel oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles. Results and comparison with other methods: Several examples are shown to illustrate the utility of the proposed method in detecting global and local spindle activity. The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F 1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4 minutes to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters. Conclusions: The proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen.

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Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea

Cited by 56 publications

References 59 publications

The impact of sleep and hypoxia on the brain

The impact of sleep and hypoxia on the brain

Analysis of EEG Sleep Spindle Parameters from Apnea Patients Using Massive Computing and Decision Tree

Multichannel Sleep Spindle Detection using Sparse Low-Rank Optimization

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