scholarly journals Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis

Physiology ◽  
2017 ◽  
Vol 32 (1) ◽  
pp. 60-92 ◽  
Author(s):  
Michael J. Prerau ◽  
Ritchie E. Brown ◽  
Matt T. Bianchi ◽  
Jeffrey M. Ellenbogen ◽  
Patrick L. Purdon
Keyword(s):  
Spectral Analysis ◽  
Spectral Estimation ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Subcortical Structures ◽  
Sleep State ◽  
Time Frequency ◽  
Oscillatory Dynamics ◽  
Spectral Estimates ◽  
The Rich

During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible—elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.

scholarly journals 0350 Characterizing Continuous Changes in Spectral Dynamics of Sleep EEG as a Function of Age

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A133-A133
Author(s):  
H Kim ◽  
M Prerau ◽  
S Redline
Keyword(s):  
Spectral Power ◽  
Population Analysis ◽  
Sampling Rate ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Apparent Difference ◽  
Sleep State ◽  
Spectral Dynamics ◽  
Time Frequency ◽  
Sleep Depth

Abstract Introduction Sleep is a continuous and dynamic physiological process. Current research practice, however, limits our ability to observe electroencephalography (EEG) oscillation dynamics by breaking sleep into discrete stages. In this study, we propose a novel quantitative framework that represents population-level changes in sleep EEG spectral dynamics as a function of age, preserving the information-rich spectral dynamics of sleep data. Rather than relying on sleep stages, our approach uses slow-oscillation power (SO-power) as an objective, continuous-valued correlate of sleep depth. Methods We analyzed the EEG signal (Fz-Cz, 256 Hz sampling rate) from a subset of the Multi-Ethnic Study of Atherosclerosis (MESA) study participants (n = 2056, 53.6% female, age: mean 69.37 ± 9.12, range 54 - 94) who underwent polysomnography. For each subject, we computed the sleep EEG multitaper spectrogram and extracted the total baseline-normalized SO-power (0.1 - 1.5 Hz). We next computed mean EEG spectral power as a function of SO-power, which we then tracked across all subjects as a function of age in sliding windows. Results The population analysis shows apparent, continuous changes in time-frequency domain features of the EEG as a function of a sleep depth along with age, that would be otherwise lost in traditional analyses. Moreover, by analyzing the directionality of the SO-power, we show that there is no apparent difference in neural activity during deepening sleep and lightening sleep; thus EEG sleep state is likely non-directional. Conclusion Our results show that state-based sleep dynamics of the EEG power spectrum can comprehensively be represented using SO-power as a surrogate of sleep depth. This representation identifies state-based activity independent of the temporal evolution of sleep architecture. As such, it is a powerful tool for analysis and phenotyping of EEG activity in large cohorts. Support The Biomedical Global Talent Nurturing Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (HI19C1065) to HK, National Institute of Neurological Disorders and Stroke (NINDS, R01 NS-096177) to MP.

New Possibilities for Analyzing Complex Asynchronous Blade Vibrations From Tip-Timing Data Using a Sparse Spectral Analysis Method

2019 ◽  
Author(s):  
Antoine Bouchain ◽  
José Picheral ◽  
Elisabeth Lahalle ◽  
Agathe Vercoutter ◽  
Bertrand Burgardt ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Spectral Estimation ◽  
Estimation Methods ◽  
Measurement Technology ◽  
Test Case ◽  
Engine Operation ◽  
Time Frequency ◽  
Mechanical Responses ◽  
Blade Vibrations ◽  
Timing Data

Abstract Measurements in engine operation provide key information for understanding the blades vibrating behaviors, especially while observing complex asynchronous vibrations, such as flutter. The studied vibration is performed by the spectral analysis of the measured vibrating mechanical responses. In this context, tip-timing measurement technology enables to get a large amount of information as it monitors all the blades. However, tip-timing technology generates sub-sampled and non-uniform sampled signals. Thus, conventional spectral estimation methods lead to aliased frequency components on the spectrum. Therefore, specific estimation methods have been developed to overcome this issue. This paper presents the tip-timing measurements analysis of a non synchronous vibration test case. It uses a new sparse method with block-OMP algorithm in order to estimate time-frequency diagrams of each blade separately. The results are compared to the results of the well-known asynchronous tip-timing method, called All Blade Spectrum. This real test case highlights the limitations of the latter method, as it involves too much uncertainty, while the sparse method with block-OMP enables to characterize these complex asynchronous blade vibrations.

Absence of sleep EEG markers in fatal familial insomnia healthy carriers: a spectral analysis study

2001 ◽  
Vol 112 (10) ◽  
pp. 1888-1892 ◽  
Author(s):  
Franco Ferrillo ◽  
Giuseppe Plazzi ◽  
Lino Nobili ◽  
Manolo Beelke ◽  
Fabrizio De Carli ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Sleep Eeg ◽  
Fatal Familial Insomnia ◽  
Analysis Study ◽  
Healthy Carriers

Sex differences in the sleep EEG of the elderly according to visual scoring and spectral analysis

2017 ◽  
Vol 40 ◽  
pp. e62-e63
Author(s):  
H. Choi ◽  
J. Jeong ◽  
H. Kim ◽  
C. Shin ◽  
I. Yoon
Keyword(s):  
Sex Differences ◽  
Spectral Analysis ◽  
The Elderly ◽  
Sleep Eeg ◽  
Visual Scoring

scholarly journals Sex Differences in the Sleep EEG of Young Adults: Visual Scoring and Spectral Analysis

SLEEP ◽  
1989 ◽  
Vol 12 (6) ◽  
pp. 500-507 ◽  
Author(s):  
Derk Jan Dijk ◽  
Domien G. M. Beersma ◽  
Gerda M. Bloem
Keyword(s):  
Young Adults ◽  
Sex Differences ◽  
Spectral Analysis ◽  
Sleep Eeg ◽  
Visual Scoring

nCPAP Treatment of Obstructive Sleep Apnea Increases Slow Wave Sleep in Prefrontal EEG

2007 ◽  
Vol 38 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Veera Eskelinen ◽  
Toomas Uibu ◽  
Sari-Leena Himanen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Period ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Sleep Stage Scoring

According to standard sleep stage scoring, sleep EEG is studied from the central area of parietal lobes. However, slow wave sleep (SWS) has been found to be more powerful in frontal areas in healthy subjects. Obstructive sleep apnea syndrome (OSAS) patients often suffer from functional disturbances in prefrontal lobes. We studied the effects of nasal Continuous Positive Airway Pressure (nCPAP) treatment on sleep EEG, and especially on SWS, in left prefrontal and central locations in 12 mild to moderate OSAS patients. Sleep EEG was recorded by polysomnography before treatment and after a 3 month nCPAP treatment period. Recordings were classified into sleep stages. No difference was found in SWS by central sleep stage scoring after the nCPAP treatment period, but in the prefrontal lobe all night S3 sleep stage increased during treatment. Furthermore, prefrontal SWS increased in the second and decreased in the fourth NREM period. There was more SWS in prefrontal areas both before and after nCPAP treatment, and SWS increased significantly more in prefrontal than central areas during treatment. Regarding only central sleep stage scoring, nCPAP treatment did not increase SWS significantly. Frontopolar recording of sleep EEG is useful in addition to central recordings in order to better evaluate the results of nCPAP treatment.

scholarly journals Application of the Lomb-Scargle Periodogram to InvestigateHeart Rate Variability during Haemodialysis

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Jill Stewart ◽  
Paul Stewart ◽  
Tom Walker ◽  
Latha Gullapudi ◽  
Mohamed T. Eldehni ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Spectral Analysis ◽  
Direct Method ◽  
Circulatory System ◽  
False Alarm Probability ◽  
Important Variable ◽  
Data Series ◽  
Spectral Estimates ◽  
Data Points

Short-term cardiovascular compensatory responses to perturbations in the circulatory system caused by haemodialysis can be investigated by the spectral analysis of heart rate variability, thus providing an important variable for categorising individual patients’ response, leading to a more personalised treatment. This is typically accomplished by resampling the irregular heart rate to generate an equidistant time series prior to spectral analysis, but resampling can further distort the data series whose interpretation can already be compromised by the presence of artefacts. The Lomb–Scargle periodogram provides a more direct method of spectral analysis as this method is specifically designed for large, irregularly sampled, and noisy datasets such as those obtained in clinical settings. However, guidelines for preprocessing patient data have been established in combination with equidistant time-series methods and their validity when used in combination with the Lomb–Scargle approach is missing from literature. This paper examines the effect of common preprocessing methods on the Lomb–Scargle power spectral density estimate using both real and synthetic heart rate data and will show that many common techniques for identifying and editing suspect data points, particularly interpolation and replacement, will distort the resulting power spectrum potentially misleading clinical interpretations of the results. Other methods are proposed and evaluated for use with the Lomb–Scargle approach leading to the main finding that suspicious data points should be excluded rather than edited, and where required, denoising of the heart rate signal can be reliably accomplished by empirical mode decomposition. Some additional methods were found to be particularly helpful when used in conjunction with the Lomb–Scargle periodogram, such as the use of a false alarm probability metric to establish whether spectral estimates are valid and help automate the assessment of valid heart rate records, potentially leading to greater use of this powerful technique in a clinical setting.

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