Odd Sound Processing in the Sleeping Brain

2008 ◽  
Vol 20 (2) ◽  
pp. 296-311 ◽  
Author(s):  
Perrine Ruby ◽  
Anne Caclin ◽  
Sabrina Boulet ◽  
Claude Delpuech ◽  
Dominique Morlet
Keyword(s):  
Paradoxical Sleep ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Oddball Paradigm ◽  
External Stimulation ◽  
Memory Processing ◽  
Stage 4 ◽  
Stage 1 ◽  
External Stimuli ◽  
First Time

How does the sleeping brain process external stimuli, and in particular, up to which extent does the sleeping brain detect and process modifications in its sensory environment? In order to address this issue, we investigated brain reactivity to simple auditory stimulations during sleep in young healthy subjects. Electroencephalogram signal was acquired continuously during a whole night of sleep while a classical oddball paradigm with duration deviance was applied. In all sleep stages, except Sleep Stage 4, a mismatch negativity (MMN) was unquestionably found in response to deviant tones, revealing for the first time preserved sensory memory processing during almost the whole night. Surprisingly, during Sleep Stage 2 and paradoxical sleep, both P3a-like and P3b-like components were identified after the MMN, whereas a P3a alone followed the MMN in wakefulness and in Sleep Stage 1. This totally new result suggests elaborated processing of external stimulation during sleep. We propose that the P3b-like response could be associated to an active processing of the deviant tone in the dream's consciousness.

scholarly journals Novel Methods for Elucidating Modality Importance in Multimodal Electrophysiology Classifiers

2022 ◽  
Author(s):  
Charles A Ellis ◽  
Mohammad SE Sendi ◽  
Rongen Zhang ◽  
Darwin A Carbajal ◽  
May D Wang ◽  
...  
Keyword(s):  
Method Development ◽  
Sleep Stage ◽  
Demographic Variables ◽  
Sleep Stages ◽  
Multimodal Classification ◽  
Sleep Stage Classification ◽  
Novel Methods ◽  
Stage 1 ◽  
Electroencephalogram Eeg ◽  
Insight Into

Multimodal classification is increasingly common in biomedical informatics studies. Many such studies use deep learning classifiers with raw data, which makes explainability difficult. As such, only a few studies have applied explainability methods, and new methods are needed. In this study, we propose sleep stage classification as a testbed for method development and train a convolutional neural network with electroencephalogram (EEG), electrooculogram, and electromyogram data. We then present a global approach that is uniquely adapted for electrophysiology analysis. We further present two local approaches that can identify subject-level differences in explanations that would be obscured by global methods and that can provide insight into the effects of clinical and demographic variables upon the patterns learned by the classifier. We find that EEG is globally the most important modality for all sleep stages, except non-rapid eye movement stage 1 and that local subject-level differences in importance arise. We further show that sex, followed by medication and age had significant effects upon the patterns learned by the classifier. Our novel methods enhance explainability for the growing field of multimodal classification, provide avenues for the advancement of personalized medicine, and yield novel insights into the effects of demographic and clinical variables upon classifiers.

scholarly journals RESEARCH OF A HIGHLY DEFECTIVE FROG CORE OF GRADE 1/9

2018 ◽  
Author(s):  
Pavels GAVRILOVS ◽  
Viktors IVANOVS
Keyword(s):  
Rail Steel ◽  
Steel Structure ◽  
Optical Emission ◽  
Research Process ◽  
Stage 4 ◽  
Stage 1 ◽  
First Time

For the first time at the Riga Technical University a study was carried out of a highly defective frog core of grade 1/9. In the course of the research an analysis of crossing piece defects on the Latvian railway was carried out in eight railway sections during years of 2015, 2016, 2017. The defect of the frog core of grade 1/9 (the 60 E1 DO 07 12 frog type) was considered according to the basic classification of the defects, and the analysis and research of the cause of its fracture were conducted from the bolt hole to the web and the base of the frog core. The research process consisted of four stages:  Stage 1: determination of metal hardness according to the Brinell scale with a modern device “Tinius O Olsen” Firmware Version 1.07, FH - 31 Series. The obtained results should be compared with the data of the manufacturer's factory – Dowlais Steel.  Stage 2: determination of the chemical composition of the rail steel of the frog core 1/9 (in the rail top, rail web, and rail base) using the ARC-MET 8000 Mobile Lab Optical Emission Spectometer Analyser. The obtained data should be compared with the manufacturer's data.  Stage 3: determination of the rail steel structure. Drawing of main conclusions about the quality of the rail steel of the frog core of grade 1/9.  Stage 4: drawing of main conclusions and summary of the cause and development of the fracture from the bolt hole of the frog core of grade 1/9.

Heat production during sleep

1984 ◽  
Vol 56 (3) ◽  
pp. 671-677 ◽  
Author(s):  
C. M. Shapiro ◽  
C. C. Goll ◽  
G. R. Cohen ◽  
I. Oswald
Keyword(s):  
Eye Movement ◽  
Heat Production ◽  
Indirect Calorimetry ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Average Heat ◽  
Stage 4

Heat production during sleep was studied by continuous indirect calorimetry with simultaneous electroencephalographic monitoring. Controlling for gross influences on heat production, comparisons of heat production during different sleep stages showed heat production in stage 4 sleep to be significantly lower than in other sleep stages. There appeared to be a gradation in heat production in non-rapid-eye-movement stages of sleep with stage 2 higher and stage 4 lower than stage 3. Heat production in stage 4 was less variable than in any other sleep stage. Both the level and variability of heat production was similar in stage 2 and rapid-eye-movement sleep. Heat production during the night was 9% lower than during resting wakefulness. The average heat production in stage 4 sleep was 14.4% lower than resting wakeful values.

Sweating responses and body temperatures during nocturnal sleep in humans

1987 ◽  
Vol 252 (3) ◽  
pp. R462-R470 ◽  
Author(s):  
J. C. Sagot ◽  
C. Amoros ◽  
V. Candas ◽  
J. P. Libert
Keyword(s):  
Rem Sleep ◽  
Slow Wave Sleep ◽  
Thermal Environment ◽  
Sweat Rate ◽  
Sleep Stage ◽  
Dew Point ◽  
Sleep Stages ◽  
Body Temperatures ◽  
Time Patterns ◽  
Stage 1

The changes in the central control of sweating were investigated in five sleeping subjects under neutral and warm conditions [operative temperature (To) = 30, 33, and 34 degrees C; dew-point temperature = 10 degrees C]. Esophageal (Tes) and mean skin (Tsk) temperatures, chest sweat rate (msw,1), and concomitant electroencephalographic data were recorded. Throughout the night, msw,1 was measured under a local thermal clamp of 38 degrees C. Results showed that the thermal environment exerted a strong influence on both the levels and the time patterns of body temperatures. Moreover, local sweating rate correlated positively with Tes, and this relationship varied according to sleep stages. For a given Tes level, there was a sleep stage-related gradation in msw,1 that was higher in slow-wave sleep (SWS) than in stage 1-2 and the lowest in rapid-eye-movement (REM) sleep. This is explained by a change in the excitability or the sensitivity of the thermoregulatory system. The msw,1 differences between stage 1-2 and SWS are accounted for by a decrease in the Tes threshold (Tset) for sweating while the slope of the msw,1-Tes relation remains unchanged. The lower msw,1 in REM sleep is explained by a lesser slope for the msw,1-Tes relation without any Tset change from stage 1-2.

scholarly journals Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies

2021 ◽  
Vol 3 ◽  
Author(s):  
Dries Van der Plas ◽  
Johan Verbraecken ◽  
Marc Willemen ◽  
Wannes Meert ◽  
Jesse Davis
Keyword(s):  
Random Forest ◽  
American Academy ◽  
Sleep Stage ◽  
Sleep Medicine ◽  
Sleep Stages ◽  
Feature Interactions ◽  
The One ◽  
Scoring Algorithms ◽  
First Time ◽  
Sleep Stage Scoring

A new method for automated sleep stage scoring of polysomnographies is proposed that uses a random forest approach to model feature interactions and temporal effects. The model mostly relies on features based on the rules from the American Academy of Sleep Medicine, which allows medical experts to gain insights into the model. A common way to evaluate automated approaches to constructing hypnograms is to compare the one produced by the algorithm to an expert's hypnogram. However, given the same data, two expert annotators will construct (slightly) different hypnograms due to differing interpretations of the data or individual mistakes. A thorough evaluation of our method is performed on a multi-labeled dataset in which both the inter-rater variability as well as the prediction uncertainties are taken into account, leading to a new standard for the evaluation of automated sleep stage scoring algorithms. On all epochs, our model achieves an accuracy of 82.7%, which is only slightly lower than the inter-rater disagreement. When only considering the 63.3% of the epochs where both the experts and algorithm are certain, the model achieves an accuracy of 97.8%. Transition periods between sleep stages are identified and studied for the first time. Scoring guidelines for medical experts are provided to complement the certain predictions by scoring only a few epochs manually. This makes the proposed method highly time-efficient while guaranteeing a highly accurate final hypnogram.

Sleep-Related Deglutition in Children

2007 ◽  
Vol 116 (10) ◽  
pp. 747-753 ◽  
Author(s):  
Kiminori Sato ◽  
Tadashi Nakashima
Keyword(s):  
Rem Sleep ◽  
Total Sleep Time ◽  
Sleep Stage ◽  
Sleep Time ◽  
Vital Function ◽  
Stage 4 ◽  
Stage 2 Sleep ◽  
Normal Human ◽  
The Mean ◽  
Stage 1

Objectives: Clearance of the pharynx by deglutition is important in protecting the airway. The pattern of deglutition during sleep was investigated in children. Methods: Ten normal human children (8.6 ± 2.9 years) were examined via time-matched recordings of polysomnography and of surface electromyography (EMG) of the thyrohyoid and suprahyoid muscles. Results: During sleep, deglutition was episodic, and it was absent for long periods. The mean number of swallows per hour (±SD) during the total sleep time was 2.8 ± 1.7 per hour. The mean period of the longest absence of deglutition was 59.7 ± 20.3 minutes. Most deglutition occurred in association with spontaneous electroencephalographic arousal in rapid eye movement (REM) and non-REM sleep. Deglutition was related to sleep stage. The mean number of swallows per hour was 27.4 ± 27.4 during stage 1 sleep, 3.1 ± 3.5 during stage 2 sleep, 2.8 ± 3.3 during stage 3 sleep, and 0.9 ± 0.8 during stage 4 sleep. The deeper the sleep stage became, the lower the mean deglutition frequency became. The mean number of swallows per hour was 2.2 ± 2.1 during REM sleep. The EMG amplitude dropped to the lowest level of recording during REM sleep. Conclusions: Deglutition, a vital function, is infrequent during sleep in children.

scholarly journals Night sleep electroencephalogram power spectral analysis in excessive daytime sleepiness disorders

1991 ◽  
Vol 49 (2) ◽  
pp. 128-135 ◽  
Author(s):  
Rubens Reimão
Keyword(s):  
Spectral Analysis ◽  
Affective Disorder ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Sleep Stage ◽  
Power Spectral Analysis ◽  
Frequency Bands ◽  
Power Spectral ◽  
Stage 4 ◽  
Stage 1

A group of 53 patients (40 míales, 13 females) with mean age of 49 years, ranging from 30 to 70 years, was evaluated in the. following excessive daytime sleepiness (EDS) disorders : obstructive sleep apnea syndrome (B4a), periodic movements in sleep (B5a), affective disorder (B2a), functional psychiatric non affective disorder (B2b). We considered all adult patients referred to the Center sequentially with no other distinctions but these three criteria: (a) EDS was the main complaint; (b) right handed ; (c) not using psychotropic drugs for two weeks prior to the all-night polysomnography. EEG (C3/A1, C4/A2) samples from 2 to 10 minutes of each stage of the first REM cycle were chosen. The data was recorded simultaneously in magnetic tape and then fed into a computer for power spectral analysis. The percentage of power (PP) in each band calculated in relation to the total EEG power was determined of subsequent sections of 20.4 s for the following frequency bands: delta, theta, alpha and beta. The PP in all EOS patients sample had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage. PP distribution in the delta range increased progressively from stage 1 to stage 4; stage REM levels were close to stage 2 levels. In an EDS patients interhemispheric coherence was high in every band and sleep stage. B4a patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in¡ every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 4; stage REM levels were between stage 1 and stage 2 levels. B2a patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 4; stage REM levels were close to stage 2 levels. B2b patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 3; stage 4 levels were close to stage 3 levels; stage REM levels were close to stage 2. B5a patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 3; stage REM levels were close to stage 2 levels, Interhemispheric coherences of B4a, B2b, and B5a groups were high in, every band and sleep stage. B4a, B2a, B2b, and B5a power spectral analysis comparison showed higher PP in B2b stage 1 alpha band, as well as, higher PP in B5a stage 2 theta band. The B4a versus. B2a power spectral analysis comparison showed higher PP in B4a stages 1 and REM alpha bands, as well as higher PP in B2a stage REM delta band.

Statistical comparison of transition sleep variables in depressed and normal subjects

1994 ◽  
Vol 9 (2) ◽  
pp. 95-100
Author(s):  
G Iorio ◽  
F Marciano ◽  
M Martino ◽  
D Kemali
Keyword(s):  
Sleep Stage ◽  
Normal Subjects ◽  
Endogenous Depression ◽  
Sleep Stages ◽  
Statistical Comparison ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
And Control ◽  
First Time ◽  
Normal Controls

SummaryWe present the values for the amount of transitions, for the total stage duration and for the first time of occurrence (latency) of a stage. A test aimed at evaluating probability values for the transition from one sleep stage to another was applied to the hypnograms of ten patients with endogenous depression and of ten normal controls. Sleep data (EEG, EMG, EOG) of each subject were recorded on magnetic tape and paper for three consecutive nights. Following sleep stage classification, portions of hypnograms of 480 min, further divided into sub-periods, were retained for the purpose of the test. The results proved that significant differences between depressed and control subjects exist among the data corresponding to various sleep stages. These findings also provided useful statistical indications in order to reduce the amount of computing needed by an automatic classifier.

Alterations in the number of motoneurons containing immunoreactive calcitonin gene-related peptide(CGRP)& choline acetyltransferase(ChAT) in the cervical spinal cord of the wobbler mouse during the development of the motoneuron disease

1995 ◽  
Vol 53 ◽  
pp. 970-971
Author(s):  
L. Vacca-Galloway ◽  
Y.Q. Zhang ◽  
P. Bose ◽  
S.H. Zhang
Keyword(s):  
Spinal Cord ◽  
Early Stage ◽  
Cervical Spinal Cord ◽  
Wild Type Mouse ◽  
Reflex Response ◽  
Mouse Strains ◽  
Behavioral Tests ◽  
Wobbler Mouse ◽  
Stage 4 ◽  
Stage 1

The Wobbler mouse (wr) has been studied as a model for inherited human motoneuron diseases (MNDs). Using behavioral tests for forelimb power, walking, climbing, and the “clasp-like reflex” response, the progress of the MND can be categorized into early (Stage 1, age 21 days) and late (Stage 4, age 3 months) stages. Age-and sex-matched normal phenotype littermates (NFR/wr) were used as controls (Stage 0), as well as mice from two related wild-type mouse strains: NFR/N and a C57BI/6N. Using behavioral tests, we also detected pre-symptomatic Wobblers at postnatal ages 7 and 14 days. The mice were anesthetized and perfusion-fixed for immunocytochemical (ICC) of CGRP and ChAT in the spinal cord (C3 to C5).Using computerized morphomety (Vidas, Zeiss), the numbers of IR-CGRP labelled motoneurons were significantly lower in 14 day old Wobbler specimens compared with the controls (Fig. 1). The same trend was observed at 21 days (Stage 1) and 3 months (Stage 4). The IR-CGRP-containing motoneurons in the Wobbler specimens declined progressively with age.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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