Directionality of coupling of physiological subsystems: age-related changes of cardiorespiratory interaction during different sleep stages in babies

2003 ◽  
Vol 285 (6) ◽  
pp. R1395-R1401 ◽  
Author(s):  
Ralf Mrowka ◽  
Laura Cimponeriu ◽  
Andreas Patzak ◽  
Michael G. Rosenblum
Keyword(s):  
Mutual Influence ◽  
Sleep Stage ◽  
Heart Rhythm ◽  
Sleep Stages ◽  
Oscillatory Systems ◽  
Strength Of Interaction ◽  
Age Related ◽  
Cardiorespiratory Interaction ◽  
The Central Nervous System ◽  
Cardiovascular Rhythms

Activity of many physiological subsystems has a well-expressed rhythmic character. Often, a dependency between physiological rhythms is established due to interaction between the corresponding subsystems. Traditional methods of data analysis allow one to quantify the strength of interaction but not the causal interrelation that is indispensable for understanding the mechanisms of interaction. Here we present a recently developed method for quantification of coupling direction and apply it to an important problem. Namely, we study the mutual influence of respiratory and cardiovascular rhythms in healthy newborns within the first 6 mo of life in quiet and active sleep. We find an age-related change of the coupling direction: the interaction is nearly symmetric during the first days and becomes practically unidirectional (from respiration to heart rhythm) at the age of 6 mo. Next, we show that the direction of interaction is mainly determined by respiratory frequency. If the latter is less than ≈0.6 Hz, the interaction occurs dominantly from respiration to heart. With higher respiratory frequencies that only occur at very young ages, the dominating direction is less pronounced or even abolished. The observed dependencies are not related to sleep stage, suggesting that the coupling direction is determined by system-inherent dynamical processes, rather than by functional modulations. The directional analysis may be applied to other interacting narrow band oscillatory systems, e.g., in the central nervous system. Thus it is an important step forward in revealing and understanding causal mechanisms of interactions.

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

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

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

scholarly journals Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Input Data ◽  
Single Channel ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Classification Performance ◽  
Sleep Stages ◽  
Layer Depth ◽  
Human Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

scholarly journals Cerebral functional networks during sleep in young and older individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Véronique Daneault ◽  
Pierre Orban ◽  
Nicolas Martin ◽  
Christian Dansereau ◽  
Jonathan Godbout ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Plasticity ◽  
Network Connectivity ◽  
Sleep Stages ◽  
Functional Networks ◽  
Older Individuals ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Age Related ◽  
Light Sleep

AbstractEven though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Low Frequency ◽  
Simple Algorithm ◽  
Sleep Stages ◽  
Spectral Indices ◽  
Current Form ◽  
Power Spectral

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

scholarly journals Light Sleep Detection based on Surface Electromyography Signals for Nap Monitoring

2022 ◽  
Vol 16 ◽  
pp. 140-145
Author(s):  
Wachiraporn Aiamklin ◽  
Yutana Jewajinda ◽  
Yunyong Punsawad
Keyword(s):  
Surface Electromyography ◽  
Sleep Stage ◽  
Low Cost ◽  
Trapezius Muscle ◽  
Sleep Stages ◽  
Upper Trapezius ◽  
Emg Signal ◽  
Power Frequency ◽  
Upper Trapezius Muscle ◽  
Waking Up

This paper proposes the development of automatic sleep stage detection by using physiological signals. We aim to develop an application to assist drivers after drowsiness or fatigue detection by a commercial driver vigilance system. The proposed method used a low-cost surface electromyography (EMG) device for sleep stage detection. We investigate skeletal muscle location and EMG features from sleep stage 2 to provide an EMG-based nap monitoring system. The results showed that using only one channel of a bipolar EMG signal from an upper trapezius muscle with median power frequency can achieve 84% accuracy. We implement a MyoWare muscle sensor into the proposed nap monitoring device. The results showed that the proposed system is feasible for detecting sleep stages and waking up the napper. A combination of EMG and electroencephalogram (EEG) signals might be yield a high system performance for nap monitoring and alarm system. We will prototype a portable device to connect the application to a smartphone and test with a target group, such as truck drivers and physicians.

nCPAP Treatment of Obstructive Sleep Apnea Increases Slow Wave Sleep in Prefrontal EEG

2007 ◽  
Vol 38 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Veera Eskelinen ◽  
Toomas Uibu ◽  
Sari-Leena Himanen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Period ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Sleep Stage Scoring

According to standard sleep stage scoring, sleep EEG is studied from the central area of parietal lobes. However, slow wave sleep (SWS) has been found to be more powerful in frontal areas in healthy subjects. Obstructive sleep apnea syndrome (OSAS) patients often suffer from functional disturbances in prefrontal lobes. We studied the effects of nasal Continuous Positive Airway Pressure (nCPAP) treatment on sleep EEG, and especially on SWS, in left prefrontal and central locations in 12 mild to moderate OSAS patients. Sleep EEG was recorded by polysomnography before treatment and after a 3 month nCPAP treatment period. Recordings were classified into sleep stages. No difference was found in SWS by central sleep stage scoring after the nCPAP treatment period, but in the prefrontal lobe all night S3 sleep stage increased during treatment. Furthermore, prefrontal SWS increased in the second and decreased in the fourth NREM period. There was more SWS in prefrontal areas both before and after nCPAP treatment, and SWS increased significantly more in prefrontal than central areas during treatment. Regarding only central sleep stage scoring, nCPAP treatment did not increase SWS significantly. Frontopolar recording of sleep EEG is useful in addition to central recordings in order to better evaluate the results of nCPAP treatment.

Neuromuscular adaptations to healthy aging

2018 ◽  
Vol 43 (11) ◽  
pp. 1158-1165 ◽  
Author(s):  
Chris J. McNeil ◽  
Charles L. Rice
Keyword(s):  
Daily Living ◽  
Healthy Aging ◽  
The Elderly ◽  
Neuromuscular System ◽  
Healthy Humans ◽  
Age Related ◽  
Physiological Adaptations ◽  
Neuromuscular Adaptations ◽  
The Central Nervous System ◽  
Stimulation Of

Even in the absence of disease or disability, aging is associated with marked physiological adaptations within the neuromuscular system. An ability to perform activities of daily living and maintain independence with advanced age is reliant on the health of the neuromuscular system. Hence, it is critical to elucidate the age-related adaptations that occur within the central nervous system and the associated muscles to design interventions to maintain or improve neuromuscular function in the elderly. This brief review focuses on the neural alterations observed at both spinal and supraspinal levels in healthy humans in their seventh decade and beyond. The topics addressed are motor unit loss and remodelling, neural drive, and responses to transcranial magnetic stimulation of the motor cortex.

High-throughput visual assessment of sleep stages in mice using machine learning

SLEEP ◽  
2021 ◽  
Author(s):  
Brian Geuther ◽  
Mandy Chen ◽  
Raymond J Galante ◽  
Owen Han ◽  
Jie Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Movement ◽  
High Throughput ◽  
Data Augmentation ◽  
Sleep Stage ◽  
Visual Assessment ◽  
Video Data ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Abstract Study Objectives Sleep is an important biological process that is perturbed in numerous diseases, and assessment its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice. Methods We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods. Results Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 +/- 0.05 (mean +/- SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to non-invasively detect that sleep stage disturbances induced by amphetamine administration. Conclusions We conclude that machine learning based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable non-invasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

COGNITIVE AGING AND COGNITIVE RESERVE: POINT OF CONTACT

2020 ◽  
pp. 256-264
Author(s):  
В. С. Мякотных ◽  
А. П. Сиденкова ◽  
Е. С. Остапчук ◽  
И. А. Кулакова ◽  
Н. А. Белых ◽  
...  
Keyword(s):  
Cognitive Aging ◽  
Cognitive Reserve ◽  
Cognitive Disorders ◽  
The Elderly ◽  
Educational Process ◽  
Special Role ◽  
Age Related ◽  
The Central Nervous System ◽  
The One

Высокий риск когнитивных расстройств у лиц пожилого и старческого возраста заставляет, с одной стороны, искать их причины, с другой - возможности профилактики. В связи с этим в последние годы получило распространение понятие когнитивного резерва, подразумевающего совокупность количественных параметров головного мозга и его способности сохранять высокую функциональную активность в процессе старения и на фоне связанной с возрастом патологии головного мозга. Представленный в статье материал на основе обзора научной литературы освещает два основных момента, касающихся возможности сохранения когнитивного резерва, - гендерный и образовательный факторы. Указывается на разные возможности женщин и мужчин, связанные со структурными и функциональными особенностями ЦНС у представителей разного пола, и на особую роль поддерживаемого в течение всей жизни образовательного процесса. Обозначена авторская позиция о необходимости разделения понятий образования и образованности, то есть уровня общей культуры и создания удобного инструмента для определения последнего. Это, в свою очередь, помогло бы в разработке модели когнитивного резерва, нацеленной на предотвращение трансформации физиологического когнитивного старения в патологическое. The high risk of cognitive disorders in the elderly and senile age makes, on the one hand, to look for their causes, on the other - the possibility of prevention. In this regard, in recent years, the concept of cognitive reserve has become widespread, implying a set of quantitative parameters of the brain and its ability to maintain high functional activity in the process of aging and against the background of age-related brain pathology. The material presented in the article on the basis of the review of scientific literature highlights two main points concerning the possibility of preserving the cognitive reserve-gender and educational factors. It is pointed to the different opportunities of women and men associated with the structural and functional characteristics of the Central nervous system in representatives of different sexes and the special role of the educational process supported throughout life. The author’s position on the need to separate the concepts of education and the level of General culture, and the creation of a convenient tool for determining the latter is indicated. This, in turn, would help in the development of a cognitive reserve model aimed at preventing the transformation of physiological cognitive aging into pathological aging.

Bladder Problems

2013 ◽  
Author(s):  
J. Eric Ahlskog
Keyword(s):  
Nervous System ◽  
Autonomic Nervous System ◽  
Neurogenic Bladder ◽  
Soft Tissues ◽  
Bladder Wall ◽  
Bladder Function ◽  
System Control ◽  
Autonomic Nervous ◽  
Age Related ◽  
The Central Nervous System

Urinary problems occur with normal aging. In women they often relate to the changes in female anatomy due to the delivering of babies. With superimposed age-related changes in soft tissues, laxity may result in incontinence (loss of urinary control), especially with coughing, laughing, or straining. In men the opposite symptom tends to occur: urinary hesitancy (inability to evacuate the bladder). This is due to constriction of the bladder outlet by an enlarging prostate; the prostate normally surrounds the urethra, through which urine passes. DLB and PDD are often associated with additional bladder problems. Recall that the autonomic nervous system regulates bladder function and that this system tends to malfunction in Lewy disorders. Hence, reduced bladder control is frequent among those with DLB, PDD, and Parkinson’s disease. This condition is termed neurogenic bladder, which implies that the autonomic nervous system control of bladder reflexes is not working properly. This may manifest as urgency with incontinence or hesitancy. Neurogenic bladder problems require different strategies than those used for treating the simple age-related problems that develop in mid-life and beyond. Moreover, there are certain caveats to treatment once a neurogenic bladder is recognized. The bladder is simply a reservoir that holds urine. It is located in the lower pelvis and is distant from the kidneys. The kidneys essentially filter the circulating blood and make the urine. The urine flows down from the kidneys into the bladder, as shown in Figure 14.1. Normally, as the bladder slowly fills with urine, a reflex is triggered when it is nearly full. This results in conscious awareness of the need to urinate, plus it primes the reflexive tendency of the bladder to contract in order to expel the urinary contents. The bladder is able to contract because of muscles in the bladder walls. Normally, nerves activate these muscles at the appropriate time, which forcefully squeeze the bladder, expelling the urine. Nerve sensors in the bladder wall are activated by bladder filling and transmit this information to the central nervous system, ramping up bladder wall muscle activity.

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