Journal articles: 'Sleep macrostructure'

1

Plazzi, Giuseppe, and Fabio Pizza. "Sleep Dynamics Beyond Traditional Sleep Macrostructure." Sleep 36, no. 8 (August 2013): 1123–24. http://dx.doi.org/10.5665/sleep.2864.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Kis, Anna, Anna Gergely, Ágoston Galambos, Judit Abdai, Ferenc Gombos, Róbert Bódizs, and József Topál. "Sleep macrostructure is modulated by positive and negative social experience in adult pet dogs." Proceedings of the Royal Society B: Biological Sciences 284, no. 1865 (October 25, 2017): 20171883. http://dx.doi.org/10.1098/rspb.2017.1883.

Full text

Abstract:

The effects of emotionally valenced events on sleep physiology are well studied in humans and laboratory rodents. However, little is known about these effects in other species, despite the fact that several sleep characteristics differ across species and thus limit the generalizability of such findings. Here we studied the effect of positive and negative social experiences on sleep macrostructure in dogs, a species proven to be a good model of human social cognition. A non-invasive polysomnography method was used to collect data from pet dogs ( n = 16) participating in 3-hour-long sleep occasions. Before sleep, dogs were exposed to emotionally positive or negative social interactions (PSI or NSI) in a within-subject design. PSI consisted of petting and ball play, while NSI was a mixture of separation, threatening approach and still face test. Sleep macrostructure was markedly different between pre-treatment conditions, with a shorter sleep latency after NSI and a redistribution of the time spent in the different sleep stages. Dogs' behaviour during pre-treatments was related to the macrostructural difference between the two occasions, and was further modulated by individual variability in personality. This result provides the first direct evidence that emotional stimuli affect subsequent sleep physiology in dogs.

APA, Harvard, Vancouver, ISO, and other styles

3

Schwarz, Johanna F. A., Torbjörn Åkerstedt, Eva Lindberg, Georg Gruber, Håkan Fischer, and Jenny Theorell-Haglöw. "Age affects sleep microstructure more than sleep macrostructure." Journal of Sleep Research 26, no. 3 (January 17, 2017): 277–87. http://dx.doi.org/10.1111/jsr.12478.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Malinowska, U., P. J. Durka, K. J. Blinowska, W. Szelenberger, and A. Wakarow. "Micro- and macrostructure of sleep EEG." IEEE Engineering in Medicine and Biology Magazine 25, no. 4 (July 2006): 26–31. http://dx.doi.org/10.1109/memb.2006.1657784.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Brandão, Luiz Eduardo Mateus, Alexandru Popa, Erasmus Cedernaes, Christopher Cedernaes, Lauri Lampola, and Jonathan Cedernaes. "0253 Consumption of a Western diet impacts sleep microstructure during normal sleep and recovery sleep – a randomized crossover trial." SLEEP 46, Supplement_1 (May 1, 2023): A113. http://dx.doi.org/10.1093/sleep/zsad077.0253.

Full text

Abstract:

Abstract Introduction While intake of specific macronutrients has been associated with changes in sleep parameters in humans – such as sleep macrostructure – direct interventional evidence is lacking. Thus, we conducted a randomized trial to examine how consumption of an unhealthy, Western diet impacts sleep at the macrostructure to microstructure level in humans. Methods In a crossover study, 15 healthy normal-weight men consumed two isocaloric diets in random order: an unhealthy high-fat, high-sugar diet, and a healthy, low-fat, low-sugar diet. Following each week-long diet, in-lab sleep was recorded using polysomnography (PSG), during a full night of sleep, and after recovery sleep occurring after extended wakefulness. Sleep duration, macrostructure and microstructure (sleep oscillatory pattern and slow waves) were investigated using machine learning-based algorithms. Results Sleep duration did not differ across the diets, based on actigraphy and the in-lab PSG. Sleep macrostructure was similar after one week on each diet. Compared with the healthy diet, the Western diet resulted in reduced delta power and slow-wave amplitude, but increased alpha and theta power, during NREM sleep. Furthermore, the Western diet also impacted the sleep oscillatory pattern in a similar direction during the recovery sleep. Conclusion Short-term consumption of an unhealthy, Western diet modulates restorative properties of sleep, and these changes persisted even into recovery sleep. Whether such changes can mediate adverse health outcomes of an unhealthy diet warrants further investigation. Support (if any) This work was supported by the Swedish Society for Medical Research (SSMF), the Swedish Research Council, and the following foundations: Diabetesfonden (2019-489), Diabetes Wellness Sverige (25-2231), Familjen Ernfors, Göran Gustafsson (2019-1941), Selander, the Swedish Cancer Foundation (19 0269 Pj); the Swedish Brain Foundation (FO2022-0704). The study sponsor/funding agencies were not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.

APA, Harvard, Vancouver, ISO, and other styles

6

Barreto, José Roberto Santiago, Regina Maria França Fernandes, and Américo Ceiki Sakamoto. "Correlation of sleep macrostructure parameters and idiopathic epilepsies." Arquivos de Neuro-Psiquiatria 60, no. 2B (June 2002): 353–57. http://dx.doi.org/10.1590/s0004-282x2002000300002.

Full text

Abstract:

Sleep and epilepsy share some common mechanisms. The objective of the present investigation was to study the macrostructure of sleep in patients with idiopathic epilepsies, focal and generalized, comparing these two groups to each other and to a control group of 12 individuals without epilepsy. A total of 35 polysomnographies were performed, 12 of them in the control group, 10 in patients with idiopathic generalized epilepsies, and 13 in patients with idiopathic focal epilepsies. Antiepileptic medications were maintained for ethical reasons. The group with idiopathic focal epilepsy showed an increase in the total recording time (p = 0.04) and the group with idiopathic generalized epilepsy had a reduction of phase 4 NREM sleep. The efficiency of total sleep period and of total sleep time was also lower in the group with idiopathic generalized epilepsy (p = 0.03 in both cases). We concluded that the group with idiopathic generalized epilepsy presents sleep of poorer quality, whereas the group with idiopathic focal epilepsy presents a tendency toward an excessive somnolence.

APA, Harvard, Vancouver, ISO, and other styles

7

González-Naranjo, Justa Elizabeth, Maydelin Alfonso-Alfonso, Daymet Grass-Fernandez, Lilia María Morales-Chacón, Ivón Pedroso-Ibáñez, Yordanka Ricardo-de la Fe, and Arnoldo Padrón-Sánchez. "Analysis of Sleep Macrostructure in Patients Diagnosed with Parkinson’s Disease." Behavioral Sciences 9, no. 1 (January 8, 2019): 6. http://dx.doi.org/10.3390/bs9010006.

Full text

Abstract:

Patients diagnosed with Parkinson’s disease present sleep disorders with a higher frequency than the general population. The sleep architecture in these patients shows variations with respect to the normal population, so in this work it was decided to investigate the characteristics of the macroarchitecture of sleep in patients diagnosed with Parkinson’s disease. A polysomnographic study was carried out on 77 patients diagnosed with Parkinson’s disease. All the studies were processed according to the AASM Manual for the Scoring of Sleep and Associated Events v.2.2, and to the criteria of the International Classification of Sleep Disorders 3rd ed. (2014). Processing was carried out using descriptive statistics, as well as non-parametric analysis for comparison between cases and controls. The group of patients showed significant reductions of the N2, N3, and REM sleep stages when compared with a control group, as well as a significant increase in intra-sleep wakefulness. The number of REM–NoREM sleep cycles and sleep efficiency showed marked reduction compared to the control group. There was a statistically significant difference in the macroarchitecture of sleep between patients diagnosed with Parkinson’s disease and healthy controls.

APA, Harvard, Vancouver, ISO, and other styles

8

Esposito, Maria, Francesco Precenzano, Ilaria Bitetti, Ilaria Zeno, Eugenio Merolla, Maria Cristina Risoleo, Valentina Lanzara, and Marco Carotenuto. "Sleep Macrostructure and NREM Sleep Instability Analysis in Pediatric Developmental Coordination Disorder." International Journal of Environmental Research and Public Health 16, no. 19 (October 2, 2019): 3716. http://dx.doi.org/10.3390/ijerph16193716.

Full text

Abstract:

Developmental Coordination Disorder (DCD) is considered to be abnormal motor skills learning, identified by clumsiness, slowness, and/or motor inaccuracy impairing the daily-life activities in all ages of life, in the absence of sensory, cognitive, or neurological deficits impairment. The present research focuses on studying DCD sleep structure and Cyclic Alternating Pattern (CAP) parameters with a full overnight polysomnography and to study the putative correlations between sleep architecture and CAP parameters with motor coordination skills. The study was a cross-sectional design involving 42 children (26M/16F; mean age 10.12 ± 1.98) selected as a DCD group compared with 79 children (49M/30F; mean age 9.94 ± 2.84) identified as typical (no-DCD) for motor ability and sleep macrostructural parameters according to the MABC-2 and polysomnographic (PSG) evaluations. The two groups (DCD and non-DCD) were similar for age (p = 0.715) and gender (p = 0.854). More significant differences in sleep architecture and CAP parameters were found between two groups and significant correlations were identified between sleep parameters and motor coordination skills in the study population. In conclusion, our data show relevant abnormalities in sleep structure of DCD children and suggest a role for rapid components of A phases on motor coordination development

APA, Harvard, Vancouver, ISO, and other styles

9

Boghez, Floriana, and Ioana Mandruta. "SCORING SLEEP: THE RULES FOR LOOKING INSIDE." Romanian Journal of Neurology 14, no. 3 (September 30, 2015): 119–24. http://dx.doi.org/10.37897/rjn.2015.3.2.

Full text

Abstract:

Polysomnography is the most comprehensive sleep study, a multi-parametric recording test (electroencephalography, electrooculogram, chin and limbs electromyogram, respiratory and cardiac functions and permanent video-recording), used as an important diagnostic tool for the sleep disorders. Sleep architecture or the sleep macrostructure is a term used to describe the divisions of sleep into specific sleep stages using electro encephalographic (EEG), electrooculographic (EOG) and electromyographic (EMG) criteria: NREM (non-rapid eye movements) stages – N1, N2 and N3, and REM (rapid eye movements) stage.

APA, Harvard, Vancouver, ISO, and other styles

10

Aptel, Florent, Perrine Canaud, Renaud Tamisier, Jean-Louis Pépin, Benjamin Mottet, Ralitsa Hubanova, Jean-Paul Romanet, and Christophe Chiquet. "Relationship Between Nocturnal Intraocular Pressure Variations and Sleep Macrostructure." Investigative Opthalmology & Visual Science 56, no. 11 (October 27, 2015): 6899. http://dx.doi.org/10.1167/iovs.15-17456.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Williams, Ellita, Anna Mullins, Omonigho Bubu, Korey Kam, Ankit Parekh, Judite Blanc, Tiffany Donley, et al. "800 Similarities of Sleep Macrostructure in Cognitively Normal Elderly and Patients with Traumatic Brain Injury." Sleep 44, Supplement_2 (May 1, 2021): A311—A312. http://dx.doi.org/10.1093/sleep/zsab072.797.

Full text

Abstract:

Abstract Introduction The stability of sleep architecture and breathing across nights can depend on factors relating to the integrity of the nervous system. Traumatic brain injury (TBI) represents a sudden-onset dysfunction of the nervous system while normal aging is associated with more gradual changes to the nervous system. While normal aging and history of TBI are both associated with sleep complaints, less is known about the stability of sleep physiology variables in these populations. Therefore, the aims of our study are to determine which sleep variables have greater night-to-night stability in separate populations of individuals with TBI and in cognitively normal older individuals. Methods All volunteers completed 2 consecutive in-laboratory nocturnal polysomnograms (NPSG). The TBI sample (N=35) comprised 71% women and 26% men (average age of 47.3 years). The cognitively normal older sample (N=78) included 74% women and 25% men (average age of 66.4 years). Descriptive statistics and intra-class correlations (ICCs) were calculated for sleep macrostructure variables (total sleep time (TST), sleep efficiency (SE), arousal index (ArI), rapid eye movement (REM), non-REM 1 & 2 (N1, N2), slow-wave sleep (SWS)), and sleep apnea including stage-specific apneas (i.e., AHI4%, AHI3A). Results Among volunteers with TBI, ICCs for sleep architecture variables were: TST (0.68), SE (0.65), ArI (0.92), %SWS (0.77), %REM (0.50), %N1 (0.83), %N2 (0.62). ICC’s for sleep apnea variables were: AHI4% (0.86), AHI3A (0.86), REM AHI4% (0.63), REM AHI3A (0.65). Among cognitively normal older volunteers, ICCs for sleep architecture variables were: TST (0.26), SE (0.29), ArI (0.80), %SWS (0.68), %REM (0.39), %N1 (0.66), %N2 (0.49). ICC’s for sleep apnea variables were: AHI4% (0.91), AHI3A (0.92), REM AHI4% (0.85), REM AHI3A (0.83). All ICCs were statistically significant in both groups, except for %N1 among cognitively normal older volunteers. Conclusion In both populations, ICC’s for arousal index were greater than for TST or SE. Likewise, ICC’s were higher in %SWS and %N1 than for %N2 or %REM. Breathing variables were more stable than architecture variables. REM-specific breathing variables showed comparatively less consistency, possibly the product of lower ICC’s for %REM sleep versus other sleep stages. Support (if any) 5T32HL129953-04, R01AG056682, R01AG066870, R21AG059179, 1RF1NS115268-01, K24HL109156, P30AG059303, P30AG066512, AASM 231-BS-20, R25HL105444

APA, Harvard, Vancouver, ISO, and other styles

12

Jung, Da Woon, Su Hwan Hwang, Yu Jin Lee, Do-Un Jeong, and Kwang Suk Park. "Apnea–hypopnea index estimation using quantitative analysis of sleep macrostructure." Physiological Measurement 37, no. 4 (March 21, 2016): 554–63. http://dx.doi.org/10.1088/0967-3334/37/4/554.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Xinyan, Marcel Smits, Leopold Curfs, and Karen Spruyt. "Sleep Respiratory Disturbances in Girls with Rett Syndrome." International Journal of Environmental Research and Public Health 19, no. 20 (October 12, 2022): 13082. http://dx.doi.org/10.3390/ijerph192013082.

Full text

Abstract:

Individuals with Rett Syndrome (RTT), a rare neurodevelopmental disorder, present disordered breathing during wakefulness. Whilst findings on breathing during sleep are contradictory, the relation between sleep breathing and their clinical features, genetic characteristics, age, and sleep phase is rarely investigated, which is the objective of this study. Overnight polysomnography (PSG) was performed. Sleep macrostructure parameters were compared between the RTT subjects with and without sleep-disordered breathing (SDB). The association between the apnea–hypopnea index (AHI) with age at PSG was tested. Particularly for RTT subjects with SDB, the respiratory indexes in REM and NREM sleep were compared. Stratified analyses per clinical characteristics, genetic characteristics, and clinical features’ severity were performed. Non-parametric statistics were applied. A sample of 11 female RTT subjects, aged 8.69 ± 5.29 years with ten confirmed with MECP2 mutations, were studied. The average AHI was 3.94 ± 1.19/h TST, of which eight (72.73%) had obstructive sleep apnea, i.e., six in 1/h TST ≤ AHI ≤ 5/h TST, and two in AHI > 5/h TST. The mean SpO2% was 81.00 ± 35.15%. The AHI was not significantly correlated with their age at PSG (rs = −0.15, p = 0.67). Sleep macrostructure in SDB-absent and SDB-present groups was not different. Respiratory indexes in those with obstructive sleep apnea showed no difference between REM and NREM sleep nor any of the strata. In our clinical sample, more than half of the RTT subjects with MECP2 mutations had obstructive sleep apnea in both NREM and REM sleep which was unrelated to their clinical features. Our results also indicated hypoxemia throughout nocturnal sleep in RTT. To conclude, our results suggest that disordered breathing during sleep is prevalently present in RTT as an independent clinical feature.

APA, Harvard, Vancouver, ISO, and other styles

14

Ramos, Regina Terse Trindade, Maria Angélica Pinheiro Santana, Priscila de Carvalho Almeida, Almério de Souza Machado Júnior, José Bouzas Araújo-Filho, and Cristina Salles. "Nocturnal hypoxemia in children and adolescents with cystic fibrosis." Jornal Brasileiro de Pneumologia 39, no. 6 (December 2013): 667–74. http://dx.doi.org/10.1590/s1806-37132013000600005.

Full text

Abstract:

OBJECTIVE: To determine the prevalence of nocturnal hypoxemia and its association with pulmonary function, nutritional status, sleep macrostructure, and obstructive respiratory events during sleep in a population of clinically stable children and adolescents with cystic fibrosis (CF).METHODS: This was a cross-sectional study involving 67 children and adolescents with CF between 2 and 14 years of age. All of the participants underwent polysomnography, and SpO2 was measured by pulse oximetry. We also evaluated the Shwachman-Kulczycki (S-K) scores, spirometry findings, and nutritional status of the patients.RESULTS: The study involved 67 patients. The mean age of the patients was 8 years. The S-K scores differed significantly between the patients with and without nocturnal hypoxemia, which was defined as an SpO2 < 90% for more than 5% of the total sleep time (73.75 ± 6.29 vs. 86.38 ± 8.70; p < 0.01). Nocturnal hypoxemia correlated with the severity of lung disease, FEV1 (rs= −0.42; p = 0.01), FVC (rs= −0.46; p = 0.01), microarousal index (rs= 0.32; p = 0.01), and apnea-hypopnea index (rs = 0.56; p = 0.01).CONCLUSIONS: In this sample of patients with CF and mild-to-moderate lung disease, nocturnal oxygenation correlated with the S-K score, spirometry variables, sleep macrostructure variables, and the apnea-hypopnea index.

APA, Harvard, Vancouver, ISO, and other styles

15

Gergely, Anna, Orsolya Kiss, Vivien Reicher, Ivaylo Iotchev, Enikő Kovács, Ferenc Gombos, András Benczúr, Ágoston Galambos, József Topál, and Anna Kis. "Reliability of Family Dogs’ Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification." Animals 10, no. 6 (May 26, 2020): 927. http://dx.doi.org/10.3390/ani10060927.

Full text

Abstract:

Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.

APA, Harvard, Vancouver, ISO, and other styles

16

Myers, A., C. Matthews, T. Kille, B. Riedner, B. Flaherty, and S. Jones. "0340 Regional Changes in Sleep Electroencephalography Power in Youth with Sleep-Disordered Breathing: A High-Density EEG Study." Sleep 43, Supplement_1 (April 2020): A129. http://dx.doi.org/10.1093/sleep/zsaa056.337.

Full text

Abstract:

Abstract Introduction Daytime neurobehavioral impairments are commonly associated with sleep disordered breathing (SDB) in children. However, a large number of studies have shown only minimal differences in sleep between children with SDB relative to control children, suggesting that sleep dysfunction is not responsible for daytime impairment. Importantly, however, previous studies have measured sleep EEG using only frontal scalp electrodes, failing to capture the regional features of sleep that are prominent during development. Here we measure sleep using hdEEG in SDB and healthy children to determine if regional sleep impairment is related to daytime neurobehavioral performance. Methods Overnight high-density electroencephalography (hdEEG, 256 channels) was recorded in 17 children with sleep disordered breathing (SDB) (age: M = 8.46, SD = 1.82, AHI: M = 11.3, SD = 8.6, 53% female) and 17 age and sex matched controls (age: M = 8.47, SD = 1.66, AHI: M = 1.5, SD = .64). Attentional capacity was assessed using the Test of Variables of Attention (TOVA) before and after sleep. Group differences in sleep macrostructure variables were assessed using unpaired t-tests. All-night spectral analysis was performed for NREM sleep and averaged across groups. Topographic differences between groups were assessed using statistical non-parametric mapping. Pearson correlations were used to determine associations between sleep and TOVA variables. Results Sleep macrostructure did not differ between groups. All-night spectral density analysis revealed a global increase in high-frequency activity in N2N3 and N3, in the alpha band (8-12 Hz, p<0.05). Global alpha power was higher in SDB youth, although this effect reached significance during N3 in a large cluster of posterior channels (N=55, p=.02). Conclusion Elevated alpha during NREM is frequently considered a correlate of nonrestorative sleep. In this sample of youth with SDB, posterior alpha is robustly increased during the deepest stage of NREM sleep. In this small sample, however, alpha power did not predict performance on an attentional task sensitive to the effects of impaired sleep. Support R21 HD092986-02 to SJ

APA, Harvard, Vancouver, ISO, and other styles

17

Öztura, I., D. Kızıltan Çelik, and B. Baklan. "0689 The Evaluation of Macrostructure and Microstructure of Sleep in Patients with NREM Parasomnias." Sleep 41, suppl_1 (April 2018): A255. http://dx.doi.org/10.1093/sleep/zsy061.688.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Peterson, B., A. Castelnovo, B. Riedner, R. Herringa, and S. Jones. "1003 Sleep Spindle Abnormalities In Youth With Ptsd." Sleep 43, Supplement_1 (April 2020): A381. http://dx.doi.org/10.1093/sleep/zsaa056.999.

Full text

Abstract:

Abstract Introduction Sleep disturbance is central to the phenomenology of PTSD across the lifespan with up to 90% of youth with PTSD reporting sleep disturbance. Subjective sleep dysfunction has also been linked to the development, maintenance and severity of the disorder. However, to date there have been no objective EEG assessments of sleep in youth with PTSD, and little is known about how the disease impacts specific sleep features. Methods Ten youth with PTSD (aged 14.5±3.2; CAPS-CA score 60.5±25.3) and ten age-and sex-matched typically developing youth (TD) (aged 14.7±3.2) completed two non-consecutive overnight high-density EEG (256-channel) polysomnography sleep studies. Prior to sleep on one night, participants performed an emotion processing task. Group differences in sleep macrostructure variables were assessed with two-way ANOVA, and group differences in all-night spectral density were assessed using unpaired t-tests. An automatic algorithm was used to detect spindle amplitude, duration, and density topographically. Statistical non-parametric mapping (SnPM) cluster testing was used to determine significantly different topographic differences between groups. Results No significant group differences were observed in sleep macrostructure variables. All-night spectral density analysis revealed increased power in PTSD youth relative to TD youth in the sigma band on both task and baseline nights. PTSD youth showed higher spindle duration, higher integrated spindle activity, and higher spindle amplitude globally both nights relative to TD youth. The increase in spindle duration achieved significance in a robust frontal cluster on both nights (43-channel cluster (p = .044) on baseline night, 66-channel cluster (p = .019) on task night). Conclusion Structural and functional abnormalities of the prefrontal cortex are a prominent feature of pediatric PTSD. The observed increase in spindle duration may represent another marker of impaired cortical function in youth with PTSD reflecting a failure of cortical inhibition of the thalamically-generated spindle rhythm. Support K08 MH100267 to RH, Wisconsin Institute for Sleep and Consciousness Pilot Award to SJ

APA, Harvard, Vancouver, ISO, and other styles

19

Mullins, A. E., N. Bagchi, A. Parekh, K. Kam, J. Wang, M. K. Williams, D. M. Rapoport, I. Ayappa, O. E. Burschtin, and A. W. Varga. "0838 Sex Specific Changes in Sleep Macro-Structure With Obstructive Sleep Apnea in a Large Clinical Population of Older Adults." Sleep 43, Supplement_1 (April 2020): A319. http://dx.doi.org/10.1093/sleep/zsaa056.834.

Full text

Abstract:

Abstract Introduction Sleep architecture is influenced by age and sex and is disrupted by obstructive sleep apnea (OSA) and periodic limb movements (PLM) of sleep. Although increasing OSA severity is thought to decrease both REM and slow wave sleep (SWS), it may do so in non-linear ways. Here, we aim to 1) compare sleep macrostructure between older men and women, 2) compare metrics of total and REM-specific OSA severity between older men and women, and 3) examine associations between metrics of OSA severity and REM sleep and SWS in a clinical sample. Methods Clinical in-lab diagnostic polysomnography (PSG) in adults ≥64 years of age from the greater New York area recorded between 2006- 2016 were collated including demographic and traditional sleep scoring metrics. Studies where TST < 4 hours were removed. Demographic, sleep macrostructure, OSA (AHI4% & AHI3A criteria), pulse oximetry (SpO2) nadir and PLM measures were compared according to sex. Results PSGs from 1282 older adults (average age 70 years in both sexes, 41% female) were included in the analyses. Women had a significantly greater SWS% (14.5 vs 7.9, p<0.001) and less N1% (18.2 vs 24.4, p<0.001), without significant differences in TST, N2%, REM%, sleep efficiency or SpO2 nadir. Men had significantly higher all-sleep OSA (median AHI4% 8.8 vs 11.1, p=0.0004; median AHI3A 24.4 vs 27.9, p=0.003) and PLM’s (4.0 vs 7.6/hour, p=0.008) but women had significantly more OSA during REM sleep (median REM AHI4% 16.7 vs 14.0, p=0.01; median REM AHI3A 32.6 vs 27.4, p=0.0002). Inverse non-linear associations were observed between OSA severity and %SWS and %REM with a unique pattern for each sleep stage. The pattern between men and women within each stage appeared similar. Conclusion In this clinical sample of older adults, women exhibit a greater proportion of SWS and worse REM-related OSA then men. Increasing OSA severity is associated with non-linear reductions in %SWS and %REM, and we plan to further investigate these relationships and sexual dimorphism by using quantitative analysis of PSG signals for more precise measures of slow wave activity and breathing physiology than traditional sleep scoring metrics. Support R01AG056682

APA, Harvard, Vancouver, ISO, and other styles

20

Marca, G. Della, C. Vollono, M. Rubino, A. Capuano, G. Di Trapani, and P. Mariotti. "A Sleep Study in Cluster Headache." Cephalalgia 26, no. 3 (March 2006): 290–94. http://dx.doi.org/10.1111/j.1468-2982.2005.01037.x.

Full text

Abstract:

Cluster headache (CH) is a primary headache with a close relation to sleep. CH presents a circa-annual rhythmicity; attacks occur preferably during the night, in rapid eye movement (REM) sleep, and they are associated with autonomic and neuroendocrine modifications. The posterior hypothalamus is the key structure for the biological phenomenon of CH. Our aim is to describe a 55-year-old man presenting a typical episodic CH, in whom we performed a prolonged sleep study, consisting of a 9-week actigraphic recording and repeated polysomnography, with evaluation of both sleep macrostructure and microstructure. During the acute bout of the cluster we observed an irregular sleep-wake pattern and abnormalities of REM sleep. After the cluster phase these alterations remitted. We conclude that CH was associated, in this patient, with sleep dysregulation involving the biological clock and the arousal mechanisms, particularly in REM. All these abnormalities are consistent with posterior hypothalamic dysfunction.

APA, Harvard, Vancouver, ISO, and other styles

21

Bouchequet, Paul, Thomas Andrillon, Geoffroy Solelhac, Alexandre Rouen, Fabien Sauvet, and Damien Léger. "0424 Visualizing insomnia phenotypes using dimensionality reduction techniques." SLEEP 46, Supplement_1 (May 1, 2023): A188—A189. http://dx.doi.org/10.1093/sleep/zsad077.0424.

Full text

Abstract:

Abstract Introduction A large number of features can be extracted from a single hypnogram, such as stages durations, onsets, or transitions probabilities. Those numerous indicators can turn a collection of sleep records into a high dimension space. Dimensionality reduction techniques are then useful to reveal patterns in data. We used 3 dimensionality reduction techniques to visualize insomnia phenotypes from a dataset of insomnia and control sleep records: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). Methods 519 sleep records have been included with the following diagnoses: 46 sleep onset insomnia, 83 sleep state misperception, 223 sleep maintenance insomnia, 117 sleep onset and maintenance insomnia and 50 controls (good sleep). We limited the feature extraction to the hypnogram as macrostructure constitute the primary source of information used for diagnosis in polysomnography. We used common macrostructure indicators such as wake after sleep onset (WASO), as well as more intricate features such as stages transitions probabilities. A first set of 54 features per hypnogram was computed. Those features were then projected in a 2 dimensional space using PCA, t-SNE and UMAP. Results Co-ranking matrix of the projections was computed for the 3 techniques (PCA: Kmax=103, Qlocal=0.38; tSNE: Kmax=20, Qlocal=0.44; UMAP: Kmax=160, Qlocal=0.44). UMAP was the technique that projected the hypnograms feature sets in the most meaningful way, by reflecting the individual diagnoses. Interestingly in the UMAP representation, group outliers, such as controls having nevertheless experienced nocturnal awakenings were projected next to similar insomnia subjects, which indicate a fine-grained capture of sleep quality at the individual level. Conclusion Dimensionality reduction and unsupervised techniques are promising methods when approaching high dimensionality spaces. A fine analysis of projected clusters could show subgroups in already known phenotypes. As literature showed robustness and applicability of those methods to larger datasets, dimensionality reduction of insomnia records could be improved by the addition of new features computed from hypnodensities generated by machine learning algorithms, or by spectral features extracted from polysomnographic signals. Support (if any) None

APA, Harvard, Vancouver, ISO, and other styles

22

Carotenuto, M., F. Precenzano, M. G. Gleijeses, M. Siciliano, F. Silvestri, M. Sabatino, F. Panico, and F. Salerno. "Sleep macrostructure in adolescents with Anorexia Nervosa: A pilot case-control study." Sleep Medicine 100 (December 2022): S206. http://dx.doi.org/10.1016/j.sleep.2022.05.556.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Seifpour, S., A. Khorrami Banaraki, M. Torabi Nami, K. Sadeghniiat Haghighi, M. Mikaili, and A. Hekmatmanesh. "Learning of emotional and nonemotional visual stimuli is related to sleep macrostructure." Sleep Medicine 16 (December 2015): S250. http://dx.doi.org/10.1016/j.sleep.2015.02.1540.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Aguilar-Andújar, M., C. Menéndez De León, I. Ramos Sánchez, L. Dinca Avarvarei, and A. Márquez Luque. "Sleep characteristics in children with specific language impairment: macrostructure and microstrucrure analysis." Sleep Medicine 14 (December 2013): e61-e62. http://dx.doi.org/10.1016/j.sleep.2013.11.114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Álvarez-Estévez, Diego, José M. Fernández-Pastoriza, Elena Hernández-Pereira, and Vicente Moret-Bonillo. "A method for the automatic analysis of the sleep macrostructure in continuum." Expert Systems with Applications 40, no. 5 (April 2013): 1796–803. http://dx.doi.org/10.1016/j.eswa.2012.09.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Mullins, Anna, Ankit Parekh, Korey Kam, Omonigho Bubu, Reagan Schoenholz, Shayna Patel, Zanetta Kovasyuk, et al. "0308 The stability of slow wave sleep and EEG microstructure measures across two consecutive nights of laboratory polysomnography in cognitively normal older adults." Sleep 45, Supplement_1 (May 25, 2022): A138—A139. http://dx.doi.org/10.1093/sleep/zsac079.306.

Full text

Abstract:

Abstract Introduction Healthy and sleep disordered populations show high night-to-night variability of polysomnographic (PSG) macrostructure metrics, however there is evidence of stability in EEG microstructure. In-laboratory PSG is critical to gold standard measures of sleep physiology but multi-night investigations are resource heavy and burdensome to participants. Given the theoretical link between sleep and Alzheimer's disease (AD) pathology (tau and β-amyloid burden), we assessed the night-to-night reliability of sleep macrostructure and EEG microstructure in a group of cognitively normal elderly participating in aging and memory studies. Methods 107 participants (mean = 67±8 yrs., range [54-84 yrs.], 72% female) attended 2 consecutive nights PSG scored according to AASM guidelines for sleep staging, respiratory and leg movement events. Midline EEG (Fz, Cz and Pz referenced to average mastoid signals) were analyzed in 98 participants using an automatic algorithm (DETOKS) for detection of relative slow wave (0.5-4Hz) activity (SWA), NREM2 spindle and K-complexes (KC) densities. Differences between night 1 and 2 for total sleep time (TST), slow wave sleep (SWS), rapid eye movement (REM), stage 2 (%NREM 2), sleep efficiency (SE), apnea hypopnea (AHI) and periodic limb movement (PLM) indices, and EEG microstructure were assessed using t-tests and Wilcoxon rank sum tests where appropriate. Two-way intraclass correlations (ICC) for single unit and absolute agreement were used to determine variability between nights for all measures. Results Night 2 PSGs showed significantly greater TST (6.3 vs 6.8 hours, p<0.001), %REM (17.5 vs 19.7, p<0.001), SE (84.9 vs 87.4%, p<0.02) and SWA (Fz:76.8 vs 78.0%, p<0.01). There were no significant differences between nights for %SWS, %NREM2, AHI, PLMs, spindle and KC densities. ICC and 95% confidence interval estimates were low for TST(0.28), %REM(0.32) and SE(0.32), moderate for %SWS(0.67) and %NREM2(0.59), good for AHI(0.78), SWA(Fz:0.86) and KCs(Fz:0.85), and excellent for PLMs(0.91) and spindles (Pz:0.97). Conclusion SWS, SWA, spindles, KC’s, AHI and PLM indices show good to excellent intra-individual stability across two consecutive nights of PSG. Although there were differences in %REM, SE and SWA, these were numerically small and perhaps functionally or clinically less significant. One night of in-lab PSG is enough to provide reliable estimates of individuals’ SWS, SWA, spindles, KC’s and sleep disorders. Support (If Any) Funding NIH R21AG055002, K24HL109156, R01AG056682, R01AG056531

APA, Harvard, Vancouver, ISO, and other styles

27

Zhang, Xin-Yan, and Karen Spruyt. "Literature Cases Summarized Based on Their Polysomnographic Findings in Rett Syndrome." International Journal of Environmental Research and Public Health 19, no. 6 (March 14, 2022): 3422. http://dx.doi.org/10.3390/ijerph19063422.

Full text

Abstract:

Rett syndrome (RTT) is a severe and rare neurodevelopmental disorder affecting mostly girls. In RTT, an impaired sleep pattern is a supportive criterion for the diagnosis, yet little is known regarding the sleep structure and sleep respiratory events. Aiming to delineate sleep by aggregating RTT case (series) data from published polysomnographic studies, seventy-four RTT cases were collected from eleven studies up until 6 February 2022 (PROSPERO: CRD 42020198099). We compared the polysomnographic data within RTT stratifications and to a typically developing population. MECP2 cases demonstrated shortened total sleep time (TST) with increased stage N3 and decreased REM sleep. In cases with CDKL5 mutations, TST was longer and they spent more time in stage N1 but less in stage N3 than those cases affected by MECP2 mutations and a typically developing population. Sleep-disordered breathing was confirmed by the abnormal apnea/hypopnea index of 11.92 ± 23.67/h TST in these aggregated cases. No association of sleep structure with chronological age was found. In RTT, the sleep macrostructure of MECP2 versus CDKL5 cases showed differences, particularly regarding sleep stage N3. A severe REM sleep propensity reduction was found. Aberrant sleep cycling, possibly characterized by a poor REM ‘on switch’ and preponderance in slow and high-voltage sleep, is proposed.

APA, Harvard, Vancouver, ISO, and other styles

28

Sixel-Döring, Friederike, Ellen Trautmann, Brit Mollenhauer, and Claudia Trenkwalder. "Age, drugs, or disease: What alters the macrostructure of sleep in Parkinson’s disease?" Sleep Medicine 13, no. 9 (October 2012): 1178–83. http://dx.doi.org/10.1016/j.sleep.2012.06.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Mendez, Martin O., Elvia R. Palacios-Hernandez, Alfonso Alba, Juha M. Kortelainen, Mirja L. Tenhunen, and Anna M. Bianchi. "Detection of the Sleep Stages Throughout Non-Obtrusive Measures of Inter-Beat Fluctuations and Motion: Night and Day Sleep of Female Shift Workers." Fluctuation and Noise Letters 16, no. 04 (November 21, 2017): 1750033. http://dx.doi.org/10.1142/s021947751750033x.

Full text

Abstract:

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

APA, Harvard, Vancouver, ISO, and other styles

30

Peřinová, Pavla, Eva Feketeová, David Kemlink, Petra Kovalská, Karolína Chlebušová, Jiří Nepožitek, Veronika Ibarburu, Eva Králíková, Soňa Nevšímalová, and Karel Šonka. "Smoking Prevalence and Its Clinical Correlations in Patients with Narcolepsy-cataplexy." Prague Medical Report 117, no. 2-3 (2016): 81–89. http://dx.doi.org/10.14712/23362936.2016.8.

Full text

Abstract:

Narcolepsy-cataplexy (NC) is a chronic neurological disease with suggested autoimmune etiopathogenesis. Nicotine stimulates central nervous system and smoking increases the risk of autoimmune diseases. Assessment of smoking habits and its correlation to clinical parameters among 87 adult NC patients (38 male, 49 female) included night polysomnography and multiple sleep latency test. In our sample, 43.7% NC patients were regular smokers, and 19.5% former smokers compared to 22.2%, and 12.6%, respectively, in the general population. Patients started to smoke in the mean age of 20.0 (SD ±6.0) years. 72.2% of NC smokers started to smoke before the onset of NC and the mean of the delay between smoking onset and NC onset was 9.1 (±5.8) years. We found a direct correlation between smoking duration and the number of awakenings, duration of N1 sleep, REM sleep latency, and apnoea/hypopnoea index (AHI), and, on the contrary, indirect correlation between smoking duration and N3 sleep duration, showing that smoking duration consistently correlates with sleep macrostructure. Smoking is highly prevalent in NC and has relationship with clinical features of NC.

APA, Harvard, Vancouver, ISO, and other styles

31

Conte, Francesca, Serena Malloggi, Oreste De Rosa, Ilaria Di Iorio, Federica Romano, Fiorenza Giganti, and Gianluca Ficca. "Sleep Continuity, Stability and Cyclic Organization Are Impaired in Insomniacs: A Case–Control Study." International Journal of Environmental Research and Public Health 20, no. 2 (January 10, 2023): 1240. http://dx.doi.org/10.3390/ijerph20021240.

Full text

Abstract:

The possibility of distinguishing insomniacs from good sleepers based on polysomnography (PSG) remains an open question. While these groups show modest differences in traditional PSG parameters, some studies suggest that finer measures may be more useful. Here we assess differences between good sleepers (GS), poor sleepers (PS) and insomniacs (IN) in classical PSG measures as well as in sleep continuity, stability and cyclic organization. PSG-monitored sleep (two nights) of 17 IN (diagnosed through a standard clinical interview; Pittsburgh Sleep Quality Index (PSQI) > 5, Insomnia Severity Index (ISI) > 14) was compared to that of 33 GS (PSQI < 5) and 20 PS (PSQI ≥ 5, ISI ≤ 14). Compared to GS, IN were impaired in sleep macrostructure (sleep latency, sleep efficiency, WASO%) and in continuity, stability and organization, whereas PS only showed disrupted continuity and stability. Spindle parameters were comparable between IN and GS, but the former displayed enhanced power in fast frequency bands. Our findings support the hypothesis of a continuum between individuals with self-reported poor sleep and insomniacs. Further, they add to extant data on impaired sleep continuity, stability and organization in poor sleepers and elderly individuals, underlining the utility of including these measures in standard sleep assessments.

APA, Harvard, Vancouver, ISO, and other styles

32

Dunbar, C., P. Catcheside, A. Vakulin, B. Zajamsek, K. Hansen, L. Lack, H. Scott, and G. Micic. "P031 Associations between sound pressure levels and amplitude modulation from wind farm noise and ambulatory recorded objective macro-sleep parameters." SLEEP Advances 2, Supplement_1 (October 1, 2021): A31. http://dx.doi.org/10.1093/sleepadvances/zpab014.079.

Full text

Abstract:

Abstract Introduction This study used ambulatory sleep studies to examine potential relationships between wind farm sound pressure level ([SPL] in dBA) and amplitude modulation (AM) on conventional measures of sleep quality in individuals residing within 10 km of a wind turbine in Australia. Methods Twenty six individuals (42:58%, females:males) aged (mean ± standard deviation) 53.2±12.2 years and residing 2.9±1.7 km from the nearest wind turbine underwent two consecutive ambulatory sleep studies and detailed indoor time-synchronised acoustic recordings inside their home. Associations between averaged whole night SPL and AM prevalence versus sleep onset latency, wake after sleep onset (WASO), percentage of sleep in each stage, sleep efficiency and total sleep time on each recording night were explored using bivariate and multiple regression analyses, using log-normalised data where required. Results Forty-five technically successful sleep studies (24 night 1, 21 night 2) were available for analysis. On night 2, AM prevalence explained 18.9% of the variance in sleep efficiency (R=.434, F(1,19)=4.421, p=0.049) and SPL explained 23.5% of the variance in WASO (R=.484, F(1,19)=5.821, p=0.026) in multiple regression analyses adjusting for age. No other sleep macrostructure variables were associated with AM prevalence or SPL on either night. Conclusion Weak relationships between SPL and AM prevalence and sleep outcomes in a real-world wind farm noise exposure setting support the need for more detailed investigations of potential wind farm noise effects on sleep quality.

APA, Harvard, Vancouver, ISO, and other styles

33

Chamorro, R., C. Algarín, M. Garrido, L. Causa, C. Held, B. Lozoff, and P. Peirano. "Night time sleep macrostructure is altered in otherwise healthy 10-year-old overweight children." International Journal of Obesity 38, no. 8 (December 19, 2013): 1120–25. http://dx.doi.org/10.1038/ijo.2013.238.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Bjurström, Martin F., Richard Olmstead, and Michael R. Irwin. "Reciprocal Relationship Between Sleep Macrostructure and Evening and Morning Cellular Inflammation in Rheumatoid Arthritis." Psychosomatic Medicine 79, no. 1 (January 2017): 24–33. http://dx.doi.org/10.1097/psy.0000000000000363.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Korkmaz, Selda, Nedime Tugce Bilecenoglu, Murat Aksu, and Tahir Kurtulus Yoldas. "Cyclic Alternating Pattern in Obstructive Sleep Apnea Patients with versus without Excessive Sleepiness." Sleep Disorders 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/8713409.

Full text

Abstract:

Background.One of the main hypotheses on the development of daytime sleepiness (ES) is increased arousal in obstructive sleep apnea (OSA). Cyclic alternating pattern (CAP) is considered to be the main expression of sleep microstructure rather than arousal. Therefore, we aimed to investigate whether there is any difference between OSA patients with versus without ES in terms of the parameters of sleep macro- and microstructure and which variables are associated with Epworth Sleepiness Scale (ESS) score.Methods.Thirty-eight male patients with moderate to severe OSA were divided into two subgroups by having been used to ESS as ES or non-ES.Results.There was no difference between two groups in clinical characteristics and macrostructure parameters of sleep. However, ES group had significantly higher CAP rate, CAP duration, number of CAP cycles, and duration and rate of the subtypes A2 (p=0.033, 0.019, 0.013, and 0.019, respectively) and lower mean phase B duration(p=0.028)compared with non-ES group. In correlation analysis, ESS score was not correlated with any CAP measure.Conclusions.OSA patients with ES have increased CAP measures rather than those without ES.

APA, Harvard, Vancouver, ISO, and other styles

36

Zucconi, Marco, Alessandro Oldani, Salvatore Smirne, and Luigi Ferini-Strambi. "The Macrostructure and Microstructure of Sleep in Patients With Autosomal Dominant Nocturnal Frontal Lobe Epilepsy." Journal of Clinical Neurophysiology 17, no. 1 (January 2000): 77–86. http://dx.doi.org/10.1097/00004691-200001000-00008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Cappellano, S., F. Testa, M. Caccamo, G. Vitrani, R. Fulgido, A. d'Aniello, D. Centonze, G. Di Gennaro, and A. Romigi. "Sleep macrostructure and microstructure in psychogenic non epileptic seizures: Comparison Between PNES and Temporal Lobe epilepsy." Sleep Medicine 100 (December 2022): S183—S184. http://dx.doi.org/10.1016/j.sleep.2022.05.495.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Sharma, Manish, Virendra Patel, Jainendra Tiwari, and U. Rajendra Acharya. "Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals." Diagnostics 11, no. 8 (July 30, 2021): 1380. http://dx.doi.org/10.3390/diagnostics11081380.

Full text

Abstract:

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

APA, Harvard, Vancouver, ISO, and other styles

39

Monda, Vincenzo, Marco Carotenuto, Francesco Precenzano, Diego Iacono, Antonietta Messina, Monica Salerno, Francesco Sessa, et al. "Neuropeptides’ Hypothalamic Regulation of Sleep Control in Children Affected by Functional Non-Retentive Fecal Incontinence." Brain Sciences 10, no. 3 (February 25, 2020): 129. http://dx.doi.org/10.3390/brainsci10030129.

Full text

Abstract:

Functional non-retentive fecal incontinence (FNRFI) is a common problem in pediatric age. FNRFI is defined as unintended loss of stool in a 4-year-old or older child after organic causes have been excluded. FNRFI tends to affects up to 3% of children older than 4 years, with males being affected more frequently than females. Clinically, children affected by FNRFI have normal intestinal movements and stool consistency. Literature data show that children with fecal incontinence have increased levels of separation anxiety, specific phobias, general anxiety, attention-deficit/hyperactivity disorder (ADHD), and oppositional defiant disorder. In terms of possible relationship between incontinence and sleep, disorders of sleep organization have been observed in the pathogenesis of enuresis so generating the hypothesis that the orexinergic system may have a crucial role not only for the sleep organization per se but also for the sphincterial control in general. This study aimed to focus on specific neurophysiological aspects to investigate on the possible relationship between sleep organizational abnormalities and FNRFI. Specifically, we aimed to measure orexin serum levels in children with FNRFI and assess their polysomnographic sleep macrostructure patterns. Two study groups were considered: FNFRI (n = 45) and typically developed (TD) (n = 45) group. In both groups, sleep patterns and respiratory events were assessed by polysomnographic recordings (PSG) during a period of two nights at least, and plasma levels of Orexin-A were measured in each participant. The findings of this initial investigation seem to support a major role of Orexin-A in sleep organization alterations in children with FNFRI. Also, our data suggest that sleep habits evaluation should be considered as screening and complementary tool for the diagnosis of fecal incontinence in children.

APA, Harvard, Vancouver, ISO, and other styles

40

Chefranov, S. G., and G. V. Kovrov. "Mathematical simulation of the macrostructure scale-similar organization of sleep and the integrative index of its effectiveness." Doklady Biological Sciences 407, no. 1 (April 2006): 131–35. http://dx.doi.org/10.1134/s0012496606020050.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

ARCE-GUEVARA, V. E., M. O. MENDEZ, J. S. MURGUÍA, A. ALBA, H. GONZÁLEZ-AGUILAR, and E. R. PALACIOS-HERNÁNDEZ. "SCALING ANALYSIS OF THE A-PHASE DYNAMICS DURING SLEEP." Fractals 28, no. 02 (March 2020): 2050050. http://dx.doi.org/10.1142/s0218348x20500504.

Full text

Abstract:

In this work, the scaling behavior of the sleep process is evaluated by using detrended fluctuation analysis based on wavelets. The analysis is carried out from arrivals of short and recurrent cortical events called A-phases, which in turn build up the Cyclic Alternating Pattern phenomenon, and are classified in three types: A1, A2 and A3. In this study, 61 sleep recordings corresponding to healthy, nocturnal frontal lobe epilepsy patients and sleep-state misperception subjects, were analyzed. From the A-phase annotations, the onsets were extracted and a binary sequence with one second resolution was generated. An item in the sequence has a value of one if an A-phase onset occurs in the corresponding window, and a value of zero otherwise. In addition, we consider other different temporal resolutions from 2[Formula: see text]s to 256[Formula: see text]s. Furthermore, the same analysis was carried out for sequences obtained from the different types of A-phases and their combinations. The results of the numerical analysis showed a relationship between the time resolutions and the scaling exponents; specifically, for higher time resolutions a white noise behavior is observed, whereas for lower time resolutions a behavior towards to [Formula: see text]-noise is exhibited. Statistical differences among groups were observed by applying various wavelet functions from the Daubechies family and choosing the appropriate sequence of A-phase onsets. This scaling analysis allows the characterization of the free-scale dynamic of the sleep process that is specific for each sleep condition. The scaling exponent could be useful as a diagnosis parameter in clinics when sleep macrostructure does not offer enough information.

APA, Harvard, Vancouver, ISO, and other styles

42

Carotenuto, Marco, Michele Roccella, Francesco Pisani, Sara Matricardi, Alberto Verrotti, Giovanni Farello, Francesca Felicia Operto, et al. "Polysomnographic Findings in Fragile X Syndrome Children with EEG Abnormalities." Behavioural Neurology 2019 (December 3, 2019): 1–8. http://dx.doi.org/10.1155/2019/5202808.

Full text

Abstract:

Fragile X syndrome (FXS) is a genetic syndrome with intellectual disability due to the loss of expression of the FMR1 gene located on chromosome X (Xq27.3). This mutation can suppress the fragile X mental retardation protein (FMRP) with an impact on synaptic functioning and neuronal plasticity. Among associated sign and symptoms of this genetic condition, sleep disturbances have been already described, but few polysomnographic reports in pediatric age have been reported. This multicenter case-control study is aimed at assessing the sleep macrostructure and at analyzing the presence of EEG abnormalities in a cohort of FXS children. We enrolled children with FXS and, as controls, children with typical development. All subjects underwent at least 1 overnight polysomnographic recording (PSG). All recorded data obtained from patients and controls were compared. In children with FXS, all PSG-recorded parameters resulted pathological values compared to those obtained from controls, and in FXS children only, we recorded interictal epileptiform discharges (IEDs), as diffuse or focal spikes and sharp waves, usually singles or in brief runs with intermittent or occasional incidence. A possible link between IEDs and alterations in the circadian sleep-wake cycle may suggest a common dysregulation of the balance between inhibitory and excitatory pathways in these patients. The alteration in sleep pattern in children with FXS may negatively impact the neuropsychological and behavioral functioning, adding increasing burn of the disease on the overall management of these patients. In this regard, treating physicians have to early detect sleep disturbances in their patients for tailored management, in order to prevent adjunctive comorbidities.

APA, Harvard, Vancouver, ISO, and other styles

43

Milman, Noah, Christina Reynolds, Nadir Balba, Yo-El Ju, and Miranda Lim. "0270 EEG Slow Wave Coherence is Related to PSG-Derived Measures of Sleep Fragmentation and Cognition in the WashU BASE Cohort." Sleep 45, Supplement_1 (May 25, 2022): A122. http://dx.doi.org/10.1093/sleep/zsac079.268.

Full text

Abstract:

Abstract Introduction There exists a bidirectional relationship between sleep disruption and neuropathology in Alzheimer’s disease (AD). Electroencephalogram (EEG) during polysomnography (PSG) provides an opportunity to examine stereotyped, coordinated brain activity. Specifically, slow wave activity (SWA), a defining feature of non-REM sleep, is aberrant in AD, and disruption of SWA in healthy adults is related to increased amyloid-beta levels. Coherence of slow waves across different cortical regions may serve as a metric of brain network coordination, sensitive to earliest AD pathology. We explored slow wave coherence during sleep in relation to sleep macrostructure and cognitive performance in the Biomarkers of Alzheimer’s Disease in Sleep and EEG(BASE) cohort. Methods EEG was collected during an attended overnight PSG from the BASE cohort (n=79, average age = 70.8 ± 4.4 years), approximately 20% of whom had Clinical Dementia Rating (CDR) of 0.5-1. A custom slow wave peak detector was implemented in MATLAB to across 6 EEG leads (C3, C4, F3, F4, O1, and O2), and slow wavecoherence was calculated as the proportion of sleep with slow waves occurring within 100ms in all 6 leads. Results EEG slow wave coherence was reliably quantified during wake, non-REM stages N1, N2, N3, and REM. Slow wave coherence in N2/N3 was negatively correlated with measures of sleep fragmentation (wake after sleep onset Spearman r = -0.3, p 0.008; number of awakenings r = -0.39, p 0.0004), and positively correlated with sleep efficiency (r = 0.28, p = 0.013). Slow wave coherence in N2/N3 was correlated with overall cognition as measured by MoCA adjusted z-score (r = 0.2, p 0.087) and executive function as measured by Trailmaking B adjusted z-score (r = 0.25, p 0.038). Conclusion Slow wave coherence was strongly correlated with measures of sleep quality. Reduced slow wave coherence was associated with poorer cognitive executive function. EEG slow wave coherence is a novel, promising approach to measure coordinated brain network function that is sensitive to early cognitive dysfunction in AD. Support (If Any) NIH R01 AG059507, ARCS Foundation Scholar

APA, Harvard, Vancouver, ISO, and other styles

44

Parekh, A. A., K. Kam, A. Mullins, A. Fakhoury, B. Castillo, Z. Roberts, L. Fleysher, D. M. Rapoport, I. Ayappa, and A. Varga. "0090 Stage-Specific Sleep Disruption and its Effect on Spatial Navigational Memory." Sleep 43, Supplement_1 (April 2020): A36—A37. http://dx.doi.org/10.1093/sleep/zsaa056.088.

Full text

Abstract:

Abstract Introduction The mechanisms by which sleep disruption impact memory may depend on sleep stage, as rapid eye movement (REM) and slow wave sleep (SWS) differ in several significant ways, including degree of neuronal synchrony and frequency of cortical local field potential oscillations. Here we sought to examine the relationship between stage-specific disruption of sleep and its effect on spatial navigational memory. Methods 9 healthy adult subjects participated in this study which involved 3 in-lab polysomnograms (normal, REM-disruption, and SWS-disruption) accompanied by pre- and post-sleep functional neuroimaging of brain during a spatial navigational memory task. Graded auditory stimuli consisting of 0.5 second bursts of high-frequency tones (300-3000Hz) were used to disrupt sleep (REM/SWS) in real time. Primary metrics to ascertain the effect of these auditory tones on sleep were time in sleep stage (REM/SWS) as a % of total sleep time (TST), bout length. The primary metric for spatial navigational memory was %change in overnight completion time on a first-person-experience 3D maze task. Results Sleep macrostructure was normal during the normal night (TST:379.9±56.6 min; SWS:19.5±7.6%; REM:19.4±5.3%; mean±std). Stage-specific disruption of sleep was achieved using auditory tones during a) SWS-disruption condition (TST:388.9±47.4 mins; SWS:6.6±4.8%; REM:18.7±5.2%) and b) REM-disruption condition (TST:365.3±69.8 mins; SWS:17.1±7.7%; REM:12.1±6.6%). SWS-disruption reduced mean bout length of SWS as compared to no disruption (1.3±0.8 mins vs. 10.3±8.2 mins; p<0.01) and REM-disruption reduced mean bout length of REM as compared to no disruption (2.2±1.7 vs. 10.6±5.2 mins; p<0.01). When sleep was not disrupted, subjects achieved overnight improvements in performance (25.3±17%) which remained unchanged during REM-disruption (18.8±29.6%, p=0.5) and during SWS-disruption (38.8±24.4%; p=0.2). Morning psychomotor vigilance was also unaffected by condition. Conclusion Stage specific disruption of sleep can be achieved using graded auditory tones. While performance on a virtual 3D maze remain unchanged with stage specific sleep disruption, lower sample size may have limited our ability to detect the change. Activation patterns from functional neuroimaging that were acquired during the spatial navigation task may elucidate the interaction between stage-specific sleep disruption and performance. Support NIH R21AG059179

APA, Harvard, Vancouver, ISO, and other styles

45

DANKER-HOPFE, HEIDI, HANS DORN, ACHIM BAHR, PETER ANDERER, and CORNELIA SAUTER. "Effects of electromagnetic fields emitted by mobile phones (GSM 900 and WCDMA/UMTS) on the macrostructure of sleep." Journal of Sleep Research 20, no. 1pt1 (February 18, 2011): 73–81. http://dx.doi.org/10.1111/j.1365-2869.2010.00850.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Djonlagic, Ina, Mengshuang Guo, Moroke Igue, Divya Kishore, Lisa Marshall, Atul Malhotra, and Robert Stickgold. "052 APOE-ε4 is associated with impaired sleep-dependent memory consolidation in healthy carriers." Sleep 44, Supplement_2 (May 1, 2021): A22. http://dx.doi.org/10.1093/sleep/zsab072.051.

Full text

Abstract:

Abstract Introduction The Apolipoprotein E (APOE)-ε4 genotype is a marker of susceptibility for late-onset Alzheimer’s disease (AD). Sleep disturbances may accelerate the aging process and increase the risk for future development of cognitive impairment and dementia. Given that the pathophysiological process of AD can predate its clinical manifestations by years or even decades, the aim of this study was to assess the role of the APOε4 allele on sleep-dependent memory consolidation in cognitively healthy adults. Methods 16 healthy APOE-ε4 carriers (mean age=49.9±13.7) and 32 healthy non-carriers (age=45.7±12.9) were included in the main analysis. Baseline screening included the Epworth Sleepiness Scale, Morningness-Eveningness scale, Wechsler Adult Intelligence Scale (WAIS) and Beck Depression Inventory (BDI), as well as a baseline polysomnogram (PSG), which also served as an adaptation night. Participants subsequently underwent an overnight testing session, which included computer sessions of the Psychomotor Vigilance Task (PVT) and the declarative Verbal Paired-Associates Task (VPA) in the evening followed by a full night PSG and repeat PVT and VPA sessions in the morning. For further reference, we added an age matched group of 16 non-carriers with newly diagnosed obstructive sleep apnea (OSA, age=48.1±15.1), who underwent the same study procedures. Results APOE-ε4 carriers had higher BDI (p=0.04) and ESS (p=0.01) scores than non-carriers. There were no significant group differences for sleep macrostructure. Evening VPA performance was similar for both groups (p=0.77). The following morning, APOE-ε4 carriers improved by 7.5±6.4 % from the evening before, compared to 13.5±7.0% for the healthy non-carriers (p=0.005). OSA subjects improved by 5.0± 8.1%, which was similar to APOE-ε4 carriers (p=0.34). Conclusion To our knowledge, this is the first study to reveal that healthy APOE-ε4 carriers while showing similar initial learning (encoding) on a declarative memory task compared to healthy controls, exhibit a deficit in sleep-dependent memory consolidation, similar to patients with obstructive sleep apnea. Consequently, this study provides evidence that impaired sleep-related memory processes could be an important harbinger in otherwise healthy individuals, who are at high risk for AD. Support (if any) NIH grant # K23HL103850 (Ina Djonlagic) and American Sleep Medicine Foundation grant #54-JF-10 (Ina Djonlagic)

APA, Harvard, Vancouver, ISO, and other styles

47

Danker-Hopfe, H., T. Eggert, G. Schmid, C. Sauter, and H. Dorn. "Effect of pulsed GSM 900 MHz, WCDMA/UMTS and tetra exposure on the macrostructure of sleep: an intra-individual perspective." Sleep Medicine 40 (December 2017): e293-e294. http://dx.doi.org/10.1016/j.sleep.2017.11.861.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Romigi, A., A. D'Aniello, M. Caccamo, F. Testa, S. Cappellano, G. Vitrani, L. Grammaldo, et al. "Effects of epilepsy surgery on sleep macrostructure and microstructure in patients with drug-resistant temporal lobe epilepsy due to hippocampal sclerosis: a prospective controlled polysomnographic study." Sleep Medicine 100 (December 2022): S179. http://dx.doi.org/10.1016/j.sleep.2022.05.481.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Scarpelli, Serena, Maurizio Gorgoni, Aurora D’Atri, Flaminia Reda, and Luigi De Gennaro. "Advances in Understanding the Relationship between Sleep and Attention Deficit-Hyperactivity Disorder (ADHD)." Journal of Clinical Medicine 8, no. 10 (October 19, 2019): 1737. http://dx.doi.org/10.3390/jcm8101737.

Full text

Abstract:

Starting from the consolidated relationship between sleep and cognition, we reviewed the available literature on the association between Attention Deficit-Hyperactivity Disorder (ADHD) and sleep. This review analyzes the macrostructural and microstructural sleep features, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (PRISMA). We included the polysomnographic studies published in the last 15 years. The results of macrostructural parameters are mixed. Almost half of the 18 selected investigations did not find differences between sleep architecture of children with ADHD and controls. Five studies observed that children with ADHD show a longer Rapid Eye Movement (REM) sleep duration than controls. Eight studies included microstructural measures. Remarkable alterations in sleep microstructure of ADHD are related to slow wave activity (SWA) and theta oscillations, respectively, during Non-REM (NREM) and REM sleep. Specifically, some studies found higher SWA in the ADHD group than controls. Similarly, higher theta activity appears to be detrimental for memory performance and inhibitory control in ADHD. These patterns could be interpreted as a maturational delay in ADHD. Also, the increased amount of these activities would be consistent with the hypothesis that the poor sleep could imply a chronic sleep deprivation in children with ADHD, which in turn could affect their cognitive functioning.

APA, Harvard, Vancouver, ISO, and other styles

50

Schirru, A., L. Carnicelli, M. Maestri, D. Crapanzano, M. Fabbrini, U. Bonuccelli, and E. Bonanni. "Disruption of sleep-wake continuum in myotonic dystrophy type I: sleep macrostructural and microstructural findings." Sleep Medicine 40 (December 2017): e295. http://dx.doi.org/10.1016/j.sleep.2017.11.866.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Sleep macrostructure'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles