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Rate and Distribution of Body Movements during Sleep in Humans

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Abstract

Body movements were measured during sleep with a mechanoelectrical transducer in 11 healthy adults. Also measured were the electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). Each subject slept alone in a quiet room for 21 to 44 consecutive nights. Body movements were classified as minor movements (actogram signal or head leads artifact), major movements (actogram signal plus head leads artifact), or movement time (MT). There was a strong relationship between rate of body movements and sleep stages, with the rate decreasing along the following sequence of stages: W greater than S1 greater than REM greater than S2 greater than S (3 + 4). If the body movements for all nights are pooled per subject, the distribution of body movement rates shows hardly any overlap for the Stages 1, REM, 2, and (3 + 4). The relative frequency of body movements seems to be regulated by a stage-dependent mechanism. The reliability of the body movement rate was determined by computing correlations between pairs of adjacent nights, which resulted in a rtt = .69. When 2 to 9 nights were pooled stepwise according to a split-half procedure, the mean rtt increased and reached values between .80 and .90, which means that body movements are a reliable sleep measure especially if the time base is large enough.
... When children experience obstructive sleep apnea or hypopnea events, their respiration significantly weaken, potentially leading to reduced blood oxygen saturation, hypoxia, and decreased heart rate [15]. Furthermore, during different sleep stages, vital signs such as respiratory rate, heart rate, and body movements exhibit distinct patterns [16]. For example, humans typically exhibit a high respiratory rate variability when awake, accompanied by the highest body movement rate. ...
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Study Objectives: To evaluate the agreement between the millimeter-wave radar-based device and polysomnography (PSG) in diagnosis of obstructive sleep apnea (OSA) and classification of sleep stage in children. Methods: 281 children, aged 1 to 18 years, who underwent sleep monitoring between September and November 2023 at the Sleep Center of Beijing Children's Hospital, Capital Medical University, were recruited in the study. All enrolled children underwent sleep monitoring by PSG and the millimeter-wave radar-based device, QSA600, simultaneously. QSA600 recordings were automatically analyzed using a deep learning model meanwhile the PSG data was manually scored. Results: The Obstructive Apnea-Hypopnea Index (OAHI) obtained from QSA600 and PSG demonstrates a high level of agreement with an intraclass correlation coefficient of 0.945 (95% CI: 0.93 to 0.96). Bland-Altman analysis indicates that the mean difference of OAHI between QSA600 and PSG is -0.10 events/h (95% CI: -11.15 to 10.96). The deep learning model evaluated through cross-validation showed good sensitivity (81.8%, 84.3% and 89.7%) and specificity (90.5%, 95.3% and 97.1%) values for diagnosing children with OAHI>1, OAHI>5 and OAHI>10. The area under the receiver operating characteristic curve is 0.923, 0.955 and 0.988, respectively. For sleep stage classification, the model achieved Kappa coefficients of 0.854, 0.781, and 0.734, with corresponding overall accuracies of 95.0%, 84.8%, and 79.7% for Wake-sleep classification, Wake-REM-Light-Deep classification, and Wake-REM-N1-N2 N3 classification, respectively. Conclusions: QSA600 has demonstrated high agreement with PSG in diagnosing OSA and performing sleep staging in children. The device is portable, low-load and suitable for follow up and long-term pediatric sleep assessment.
... These low frequency components could be unwanted components due to body movements creating artifacts below 1 Hz [114]. To identify whether the frequencies are caused by body movements, a suggestion would be to investigate whether the rate of body movement is stronger during N1 and REM than for N2 and N3 [115]. ...
Thesis
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Dreams reflect our general well-being, with symptoms of mental health disorders often manifesting in our dream content. Dream analysis can be a powerful tool for identifying early symptoms and monitoring mental health disorders. Developing a tool capable of automatic retrieval of dream con- tent from sleeping subjects - a dream decoder - would allow for the retrieval of many more dream experiences, allowing better understanding of a subject’s mental well-being. Brain activity can be measured using electroencephalography (EEG), a relatively inexpensive, non- invasive technique with high temporal resolution. With this technique, we can investigate recordings of brain activity during dream experiences and compare them to recordings of no experience, inves- tigating what dreams are. This thesis is an initial contribution towards the dream decoder. The first step is to identify when a subject is experiencing a dream. In this work, we experiment with deep learning (DL) and machine learning (ML) to investigate to what extent it is possible to identify dream experiences from EEG sig- nals. Principal component analysis (PCA) and common spatial pattern (CSP) in combination, and power spectral density (PSD), are used for feature extraction. Multivariate empirical mode decompo- sition (MEMD) is applied to improve the classification. Furthermore, we attempt to define the most important regions for identifying dream experiences using permutation-based channel selection and PSD representation, from which channel subsets are identified for further classification. Two datasets are used: The Zhang & Wamsley (ZW) and the Tononi dataset, both publicly available through the Dream EEG and Mentation (DREAM) database. They contain EEG data collected be- fore awakening, paired with a verbal report of any dream experiences from which the recordings are labeled. Recordings are made during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. The results indicate that dream experiences can be identified from EEG signals using ML. For the Tononi dataset using 256 EEG channels, we obtain up to 0.993 accuracy, 0.994 F-score, and 0.987 kappa score using MEMD, PSD, and the extreme gradient boosting (XGB) classifier. For the ZW data using 58 EEG channels, during NREM and REM sleep, we obtain 0.94 accuracy, 0.96 F-score and 0.84 kappa, and accuracy 0.998, F-score 0.998 and kappa 0.995, using MEMD, PCA, CSP, and the k-nearest neighbor (KNN) classifier. Firm conclusions on important brain regions are not readily available, but the results indicate areas in the parietal and frontal lobe.
... lization (sleeping) by the movement of their bodies from right to left sides and vice versa. Based on the story, people were contemplating that they are awake, but they were asleep instead. People move on their right and on their left sides during sleep and it has been reported that people have several types of body movements during sleep (Johanna Wilde-Frenz et. al., 1983). ...
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Islam emphasizes on prevention is better than cure concept. The holy Quran itself emphasizes on disease prevention based on several verses which upholds the concept as well as curing a disease. Complexity of Al-Quran in this field is obvious but the verses clearly depict processes which are scientifically valid during reviews. As an objective, this article aims to explore the concept of preventing pressure injury by focusing on critical care patients whom are commonly admitted to the intensive care unit (ICU). The verse of Al-Quran from surah Al-Kahfi clearly states regarding this issue indirectly. It has also revealed that Ashabul Kahfi (people of cave) whom have slept for 309 years, were not complicated with pressure injury which is the most common issue in critically ill or bedridden patient. The study is qualitative in nature in which the researcher will focus on textual and comparative analysis. As an analytical view, the concept of repositioning to prevent pressure injury especially in critical care subject, in the current medical practice, has been closely related to Al-Quran verses.
... The advance of technology has enabled the employment of smartphone sensors by several groups to conduct sleep studies [17][18][19]. The accelerometer sensor, embedded in every smartphone, has been utilised to measure phone and body movement in order to monitor sleep stages [20]. ...
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This study emphasises the critical role of quality sleep in physical and mental well-being, exploring its impact on bodily recovery and cognitive function. Investigating poor sleep quality in approximately 40% of individuals with insomnia symptoms, the research delves into its potential diagnostic relevance for depression and anxiety, with a focus on intervention in mental health by understanding sleep patterns, especially in young individuals. This study includes an exploration of phone usage habits among young adults during PPI sessions, providing insights for developing the SleepTracker app. This pivotal tool utilises phone usage and movement data from mobile device sensors to identify indicators of anxiety or depression, with participant information organised comprehensively in a table categorising condition related to phone usage and movement data. The analysis compares this data with survey results, incorporating scores from the Sleep Condition Indicator (SCI), Patient Health Questionnaire-9 (PHQ-9), and Generalised Anxiety Disorder-7 (GAD-7). Generated confusion matrices offer a detailed overview of the relationship between sleep metrics, phone usage, and movement data. In summary, this study reveals the accurate detection of negative sleep disruption instances by the classifier. However, improvements are needed in identifying positive instances, reflected in the F1-score of 0.5 and a precision result of 0.33. While early intervention potential is significant, this study emphasises the need for a larger participant pool to enhance the model’s performance.
... Furthermore, LMM duration showed an inverse correlation with indices, i.e. the higher the index, the shorter the duration. This is in line with historical results, which reported a similar pattern of the relationship between body movements rate and sleep stages (W > N1 > REM > N2 > N3 [S3 + S4]), despite the different scoring methodologies [32]. Of note, the change in LMM duration and frequency was sleep stage dependent. ...
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