Search results for "Sleep Stages"
showing 7 items of 57 documents
Automatic recognition of rapid eye movement (REM) sleep by artificial neural networks.
1995
Artificial neural networks are well known for their good performance in pattern recognition. Their suitability for detecting REM sleep periods on the basis of preprocessed EEG data in humans under clinical conditions was tested and their performance compared with the manual evaluation. A single channel of the EEG signal was analysed in time periods of 20 s and preprocessed into a vector of six real numbers, which served as input to the network. EOG and EMG information was ignored. Backpropagation was used as a learning rule for the network, which consisted of 12 neurons and 39 synapses. Training datasets were put together from the input vectors and the corresponding sleep stages were scored…
Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network
2021
Polysomnography (PSG), the gold standard for sleep stage classification, requires a sleep expert for scoring and is both resource-intensive and expensive. Many researchers currently focus on the real-time classification of the sleep stages based on biomedical signals, such as Electroencephalograph (EEG) and electrooculography (EOG). However, most of the research work is based on machine learning models with multiple signal inputs or hand-engineered features requiring prior knowledge of the sleep domain. We propose a novel encoded Time-Distributed Convolutional Neural Network (TDConvNet) to automatically classify sleep stages based on a single raw PSG signal. The TDConvNet can infer sleep st…
No Effects of Pulsed High-Frequency Electromagnetic Fields on Heart Rate Variability during Human Sleep<sup>1</sup>
1998
The influence of pulsed high-frequency electromagnetic fields emitted by digital mobile radio telephones on heart rate during sleep in healthy humans was investigated. Beside mean RR interval and total variability of RR intervals based on calculation of the standard deviation, heart rate variability was assessed in the frequency domain by spectral power analysis providing information about the balance between the two branches of the autonomic nervous system. For most parameters, significant differences between different sleep stages were found. In particular, slow-wave sleep was characterized by a low ratio of low- and high-frequency components, indicating a predominance of the parasympathe…
Impact of an oral appliance on obstructive sleep apnea severity, quality of life, and biomarkers
2017
OBJECTIVE/HYPOTHESIS To investigate outcomes including efficacy, quality of life, and levels of inflammatory markers of a mandibular advancement device (MAD) for moderate-to-severe obstructive sleep apnea (OSA). STUDY DESIGN Case-control study. METHODS Patients with apnea-hypopnea index (AHI) ≥ 15/hr who only accepted MAD therapy (study group) or who refused any treatment (control group) were recruited. At baseline and at 6 months, polysomnography, Epworth Sleepiness Scale (ESS), Functional Outcomes of Sleep Questionnaire (FOSQ), C-reactive protein (CRP), interleukin 1β, interleukin 6, and tumor necrosis factor α (TNF-α) were assessed in both groups. RESULTS At baseline, the study group (n …
EEG-responses caused by environmental noise during sleep their relationships to exogenic and endogenic influences.
1978
Abstract At a certain level of intensity acoustical stimuli occurring during the night lead to sleep disorders. Whereas presumed after-effects (decrease of performance, functional and organic diseases) can as yet not be related to noise, it is evident that the primary effects which can be recorded immediately after stimulus onset are caused by noise. Because of the small number of experimental trials carried out in different investigations, the results of each single paper can only be tentative. Therefore — concerning awakening reactions and reactions less than a change of at least one sleep stage — the data from publications of comparable method and evaluation have been summarised. With th…
LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.
2021
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.g., wearable sleep monitoring devices), there is a need for simple and compact models. In this paper, we propose a lightweight model, namely LightSleepNet, for rapid sleep stage classification based on spectrograms. Our model is assembled by a much fewer number of model parameters compared to…
Intensity of Respiratory Cortical Arousals Is a Distinct Pathophysiologic Feature and Is Associated with Disease Severity in Obstructive Sleep Apnea …
2021
Background: We investigated whether the number, duration and intensity of respiratory arousals (RA) on C3-electroencephalographic (EEG) recordings correlate with polysomnography (PSG)-related disease severity in obstructive sleep apnea (OSA) patients. We also investigated if every patient might have an individual RA microstructure pattern, independent from OSA-severity. Methods: PSG recordings of 20 OSA patients (9 female