Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep

Terzano, Mario Giovanni; Parrino, Liborio; Sherieri, Adriano; Chervin, Ronald D.; Chokroverty, Sudhansu; Guilleminault, Christian; Hirshkowitz, Max; Mahowald, Mark; Moldofsky, Harvey; Rosa, Agostinho; Thomas, Robert J.; Walters, Arthur S.

doi:10.1016/s1389-9457(01)00149-6

Cited by 587 publications

(360 citation statements)

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“…Outside of prosthesis control, other applications of EMG pattern recognition for which multi-modality data sets exist include, for example, sleep studies, such as the Cyclic Alternating Pattern (CAP) Sleep Database [49] and the Sleep Heart Health Study (SHHS) Polysomnography Database [50]; biomechanics, such as the cutting movement dataset [51] and the horse gait dataset [52]; and brain computer interfaces, such as the Affective Pacman dataset [53] and the emergency braking assistance dataset [54]. Recently, emotion recognition using multiple physiological modalities has gained attention as another important application that has benefited from the incorporation of surface EMG.…”

Section: Multiple Modalitiesmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

173

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Section: Multiple Modalitiesmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

173

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“…CAP is essentially an EEG expression of the arousal level instability during NREM sleep, characterized by the periodic occurrence of arousal-related phasic EEG events, including cortical arousals [11]. We recently suggested the presence of a relation between CAP parameters and GH therapy in children with Prader-Willi syndrome (PWS).…”

Section: Introductionmentioning

confidence: 99%

NREM Sleep Architecture and Relation to GH/IGF-1 Axis in Laron Syndrome

Verrillo

Bizzarri²,

Cappa³

et al. 2010

Horm Res Paediatr

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Background: Laron syndrome (LS), known as growth hormone (GH) receptor deficiency, is a rare form of inherited GH resistance. Sleep disorders were described as a common feature of adult LS patients, while no data are available in children. Bi-directional interactions between human sleep and the somatotropic system were previously described, mainly between slow wave sleep and the nocturnal GH surge. Aims: To analyze the sleep macro- and microstructure in LS and to evaluate the influence of substitutive insulin-like growth factor 1 (IGF-1) therapy on it. Methods: Two young LS females underwent polysomnography; the first study was performed during IGF-1 therapy, the second one after a 3-month wash-out period. Results: In both patients, the sleep macrostructure showed that time in bed, sleep period time, total sleep time, sleep efficiency and rapid eye movement (REM) percentage were all increased during wash-out. The sleep microstructure (cyclic alternating pattern: CAP) showed significantly higher EEG slow oscillations (A1%) in NREM sleep, both during IGF-1 therapy and wash-out. Conclusions: Sleep macrostructure in LS children is slightly affected by substitutive IGF-1 therapy. Sleep microstructure shows an increase of A1%, probably related to abnormally high hypothalamic GHRH secretion, due to GH insensitivity.

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“…The 8 healthy subjects included in the study did not present any neurological disorders and were free of drugs that affect the central nervous system. Furthermore, the sleep stages (W = wake, S1-S4 = sleep stages, R = REM) were coded by expert neurologists, which is in accordance with the R&K rules 21 . In this work, the sleep stages were analysed based on P4-O2 and the maximum available sample size was 120.…”

Section: Methodsmentioning

confidence: 99%

Improved GP algorithm for the analysis of sleep stages based on grey model

Li¹,

Xie²,

Zhao³

et al. 2017

ScienceAsia

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Correlation dimension analysis of EEG signals is widely used to access sleep stages. However, the standard Grassberger-Procaccia (GP) algorithm used to calculate the correlation dimension is very time consuming. To overcome this problem, an algorithm that combines the grey model and GP algorithm (GM-GP) is proposed. The results show that the correlation dimensions computed from GP and GM-GP are highly correlated, and the significance between the CDs in different stages of GM-GP is similar to GP. Furthermore, the computation time of the proposed method is at most 5% of that of the GP. The proposed algorithm is suitable for the real-time monitoring of sleep stages, which can provide a deeper understanding of brain function.

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Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep

Cited by 587 publications

References 0 publications

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Recognition in the Era of Big Data and Deep Learning

NREM Sleep Architecture and Relation to GH/IGF-1 Axis in Laron Syndrome

Improved GP algorithm for the analysis of sleep stages based on grey model

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