Using an extended set of features the first method achieves an accuracy of 72.8%, 77.4%, and 80.3% in the detection of wakefulness, REM, and NREM states, respectively, and the second an estimation error of 4.3%, 9.8%, and 5.4% for the SE, REM, and NREM percentages, respectively.Two minute window of the RR signal (top left) and the respective power spectrum (top right) showing two peaks centered in the LF and HF bands. In [26] and [14], the authors present a sleep,influence of obstructive sleep apnea (OSA) in the performance,eters are initially estimated from the output of three binary classifiers, fed to a,HMM based algorithm (left). However, continuous sensing constitutes a major source of energy consumption; on the other hand, lowering the sensing rate may lead to missing the detection of critical contextual events. [Online; accessed 20-September-2012]. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.An increasing number of customers are using wearable devices, sleep trackers, or smartphones apps to monitor and measure a variety of body functions. SJL and chronotypes have been widely studied in West countries, but never been described in China. The estimation errors are 4.3% for the SE, 9.8% for the,In order to test the influence of the training and test sets,and to assess the generalization capability of the algorithm the.five datasets and the average error computed.This procedure was repeated ten times resulting in average,The automatic estimation of a hypnogram is often limited by,noisy observations that need to be discarded. So far, we have been mainly focused in mapping and measuring the level of expression of specific proteins, as well as in the identification of cell cycle stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. (1999, Dec.). These highly constrained condi-,and limits the duration of the typical exam, which is usually,Due to these constraints, simpler alternatives have been sug-,gested to complement the information given by the PSG. Biol. Mais cette finale de Coupe de France, dans ce monde du quasi-silence, n'a pas pris la moindre ampleur, en choisissant un vainqueur parcimonieux et en laissant pour marque majeure la blessure spectaculaire et inquiétante de Kylian Mbappé. Long-term study of the sleep of insomnia pa-.tients with sleep state misperception and other insomnia patients.http://ajp.psychiatryonline.org/cgi/content/abstract/149/7/904.ysis,” Ph.D. dissertation, St Cross College, Dept. A possible approach to overcome this limitation is,an ensemble of classifiers, trained with a rejection option and a,HMM based regularization algorithm, which takes into account,statistical information regarding the sleep cycle. SAINT-ETIENNE-PARIS SG . Paris FC remained in Ligue 1, while PSG were administratively relegated to Division 3. In order to extract these features from each RR,an eight-order autoregressive model (AR) [32] is fitted to the,ture the low-frequency components of the RR signal. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. Several algorithms were shown to be able to automatically score sleep stages based on HRV, typically meas- ured with electrocardiogram (ECG), often in combination with respiratory effort [21],To find the influence of obesity on cognition before and after weight loss nd according with aging,Biomedical molecular imaging became an essential complementary approach to high-throughput technologies in disease diagnosis, prognosis and therapy selection. Recently, new wearables devices have been improved by adding the function of monitoring autonomic activities such as heart rate and pulse wave using photoplethysmography [22] or electrocardiogram sensors [23]. This chapter discusses the main findings reported in literature with special focus on the dynamics of heart rate and respiration.Monitoring context depends on continuous collection of raw data from sensors which are either embedded in smart mobile devices or worn by the user. This is particularly,devices. The optimal solution,the most probable state sequence, is computed using the,The three considered sleep parameters are computed from the,The estimation of the sleep parameters, as described in the,previous section, follows the standard procedure, where the,computation is performed directly from the hypnogram. No gender differences are found in chronotypes. Découvrez le classement et les scores en live : Ligue 1 2009-2010 sur Eurosport. Personal use is permitted, but republication/redistribution requires IEEE permission.breathing process is thus a direct reflection of the activity of the,In [14], the authors show that respiration is more irregular.during REM states when compared to nonREM, and in [15],the authors show that different sleep stages lead to distinct au-,The automatic extraction of useful indicators for sleep disor-,ders diagnosis, using data acquired in mobile environments, is,still an open issue that poses many challenges. (1992). 246–263, 2010. Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc. In 2013, Ebrahimi et al. Med. The classification performance for the test set was specificity = 0.851, accuracy = 0.793 and sensitivity = 0.702.The aim of this study was the optimization of Time-Variant Autoregressive Models (TVAM) for tracking REM - non REM transitions during sleep, through the analysis of spectral indexes extracted from tachograms. Approach: The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (Wake/N1+N2/N3/REM) and three-class (Wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which with a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA). For 'regular' participants, CRFt achieved a median accuracy and Cohen's kappa of 67.0% and 0.51, 70.8% and 0.53 and 81.3% and 0.62 for five-, four- and three-classes respectively, and for 'OSA' patients, of 60.0% and 0.40, 70.0% and 0.45, and 75.8% and 0.51 for five-, four- and three-classes respectively. This method improves the accuracy of the,estimated sleep parameters by taking into account 1) the higher,accuracy of the binary classifiers, compared to the full hypno-,gram estimation, 2) a correction factor, computed in the training,step, that takes into account the percentage of misclassified sam-,ples, and 3) an estimation of the number of samples that were,which maps each sample into one of three labels,the fraction of rejected samples per class (,The countingoperation can thus be improved by correcting,the number of predicted samples in each class as,and estimating the number of rejected samples from each class,The expressions for the three sleep parameters can now be,where SE is computed from the output of the SW classifier and,The algorithm for hypnogram estimation was tested with sev-,eral RFs, the obtained results are listed in T,10% yields the highest values for almost all the FOMs, achieving.a detection ratio of 72.8%, 77.4%, and 80.3% for wakefulness,This result (obtained with data previously unseen by the classi-,fiers) corresponds to a gmean of 76.8% and a kappa index of,For performance comparison purposes, the hierarchical,classification method, with no data rejection, discussed in,classifiers wake/sleep and REM/NREM have relati,performances reported in [27] for sleep/wake discrimination. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. In large biomedical datasets, such,as the one considered, the systematic rejection of unreliable,segments and/or samples has been shown to increase the ac-,curacy of the classification procedures without compromising,the overall result [24]. Objective: 78: 32 13 7 12 55 41 +14 9: AJ Auxerre rés. SAMEDI 17 FEVRIER 2019. Each set of features is com-,mixture model (RMM) [36] distribution fitted to ACT,In order to minimize the interpatient variability,normalization step ensures that all features fall in the range,The discriminative power of each feature w,significance was assessed performing a one-way ANOV,significance level of 0.05) obtained for each feature in four dif-,The discrimination between the considered classes, wake-,fulness, REM, and NREM, falls within a common multiclass,classification problem. This is motivated by the,Hz. Non-subject-specific wake versus sleep classification resulted in a Cohen's kappa value of 0.695, while REM versus NREM resulted in 0.558 and N3 versus N1N2 in 0.553. 2 illustrates the steps in the computation of,based on the work described in [35]. In addition, we will evaluate the extension of the multiple-class sleep stage classification problem to the five classes defined by the American Academy of Sleep Medicine (AASM) (Iber et al. Heart period variability in sleep.rate variability during a 105-day simulated mission to mars,”,http://www.ncbi.nlm.nih.gov/pubmed/21658979,C. The proposed,method is able to estimate a three-state hypnogram with an,sleep parameters from this hypnogram, particularly REM, and,nonREM percentages, is strongly affected by the estimation,In order to solve this problem, we describe a method that,discards ambiguous samples and estimates the sleep parameters,based on the information regarding classifiers performance and,rejection patterns. It is well known that wearable devices measuring sleep based only on accelerometers overestimate sleep duration as they cannot well distinguish between sleeping from lying quietly [17][18][19][20][21]. Sainté 585 6. OM 645 4. It is largely regulated by the circadian clock but constrained by work obligations to specific sleep schedules. The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%. The core of these devices is a 3-D accelerometer,that measures the acceleration along three orthogonal axes with,a configurable output format. Results SJL follows a normal distribution and 17.07% (12151/71176) of Chinese have SJL larger than 1-hour. In this paper, a HMM-based algorithm is described to,overcome this limitation and compute sleep parameters from a,The hypnogram estimation algorithm achieves an accuracy.of 78.3% with similar detection rates for all considered states.knowledge, among the highest values reported in the literature,for a three state discrimination task: [21] (,of the cited methods discard noisy observations, preventing the,estimation of a continuous hypnogram, and do not take into.account the inherent temporal correlation between sleep states.scorer agreement in the hypnogram estimation is approximately,83%. document titled PSG COLLEGE OF TECHNOLOGY is about AI and Robotics We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. We demonstrate that it is important to remove respiratory influences during classification of rest and mental stress. People of later chronotypes and long sleepers suffer more from SJL.BACKGROUND Based on electroencephalographic recordings and characteristic patterns and waveforms we can distinguish wakefulness and five sleep stages grouped into light sleep, deep sleep, and rapid-eye-movement (REM) sleep. However, wearable devices measuring sleep based only on accelerometers overestimate sleep duration as they cannot really distinguish sleeping from lying quietly [17][18][19][20][21]. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. Bordeaux 535 9. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. Additionally, and albeit with a decrease in performance when compared with healthy participants, sleep stage classification in OSA patients using cardiorespiratory features and CRFt seems feasible with reasonable accuracy.The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. Time-Varying Autoregressive Models (TVAMs) were used as feature extractor while Hidden Markov Models (HMM) was used as time series classifier. Le PSG a remporté sa treizième Coupe de France en battant péniblement Saint-Étienne (1-0), dans un décor triste, après une très faible finale marquée par la blessure de Mbappé par Perrin. If the performance is assessed only for movement periods this improvement is even higher.An alternative DSS which models the behaviour of the Heart Rate Variability (HRV) signal linked to stable (NREM) and instable (REM) cerebral waves during sleep and a probabilistic model of the sleep stages transitions for decision was developed. Evaluating these signals in 30 sec- onds interval is,This paper describes the design and validation of an effective sleep stage classification strategy for patients with sleep apnea. Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively). ... La Sister Cities Cup a été organisée du 17 au 22 mai 2010 à Chicago. :H Y P N O G R A MA N DS L E E PP A R A M E T E RC O M P U T A T I O NF R O MA C T I V I T YA N DC A R D I O V A S C U L A RD A T A 1 7 1 9 IEEE Annu. Random forest was used to characterize which factors (age, BMI, sex, nocturnal and daytime sleep durations and exercise) mostly contribute to SJL and MSFsc. The following features are extracted:The positive detection rate is computed as,represent a sleep parameter, the estimation error is given by,0.62). Using the detrended fluctuation analysis up to the fourth order we find that breath-to-breath intervals and breath volumes separated by several breaths are long-range correlated during the REM stages and during wake states. Voici ainsi le classement de la Ligue 1 sur la décennie 2010-2019, un classement qui comprend tous les résultats des clubs de Ligue 1 depuis 10 ans.MERCATO : Les 10 infos et rumeurs de transferts les plus chaudes du lundi 21 septembre,Minute Media 2019 90min © Tous droits réservés,Mercato : Le PSG sur le point de gagner son duel face à Man City pour Koulibaly,MERCATO : Les détails de l'offre de la Juventus pour Alvaro Morata. The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. (NREM) sleep with a predominance of parasympathetic output.In [11], the authors present a brief retrospective of the study of,[12], and in [13], an in depth review of the relationship between,The respiration process is controlled by a cyclic stimulation,of the diaphragm mediated by the phrenic nerve, which contains.0018-9294 © 2014 IEEE. To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (Wake/N1/N2/N3/REM), to explore the benefit of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients. With this new method the estimation errors,iological Study of sleep. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. classes. One of the most important prob- lems in ECG analysis is the extraction of appropriate features, and this can be tackled in various ways. He used a standard2 that κ less than 0.40 represents "poor" agreement and a finding that few κ coefficients greater than 0.40 are reported in the literature on peer assessments. All the epochs correspond-,ing to any of the three distinct NREM sleep stages were grouped.features and the extraction procedures are the following.computed, according to the guidelines from [8], in the LF and,HF bands. Rennes 527 10. Adolescents are later types compared to adults. Eng. Chinese have smaller SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. This response is used to bandpass filter the RR,signal, resulting in the signal and power spectrum displayed in the bottom left,two oscillators, with 1 corresponding to perfect synchronization,with the bandpass IIR filter described by the set of optimal.the signal is filtered in both forward and backward direction.Fig. Int. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.time consuming even for experience physician. It is uncomfortable to the subject and is usu-,ally done in clinical facilities. Recently, new wearables devices have been improved by adding the function of monitoring autonomic activities such as heart rate and pulse wave using photoplethysmography [22] or electrocardiogram sensors [23]. The capability to differentiate sleep stages in predefined categories (wake, light sleep, deep sleep, REM) was successful in 65%. In addition, it is important to stress that many., vol. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.Objective: Significance: The results suggest that CRFt is not only better at learning and predicting more complex and irregular sleep architectures, but that it also performs reasonably well in five-class classification, the standard for sleep scoring used in clinical PSG. In most cases, the methods used are strongly operator-dependent and the visual information is merely qualitative with no possibility to extract numerical quantification. Soc.Proceedings of the 8th International Conference on Indepen-.Background: Recently, new wearables devices have been improved by adding the function of monitoring autonomic activities such as heart rate and pulse wave using photoplethysmography [22] or electrocardiogram sensors [23]. Conclusions: A review was conducted on PubMed. VCAMS is validated using multiple experiments, which include evaluation of model success when considering binary and multi-user states. (2007, Oct.). The problems to,curate estimation of sleep and wakefulness periods, detection of,REM and NREM sleep, and the automatic computation of,these issues. All these methods are based on analy- sis of a Tachogram (record of RR intervals). Classement de @Ligue1Conforama, 2010-2019 : 1. Soc. We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. An alternative method for the estimation of the,sleep parameters is also described based on the output of two binary classifiers,obtaining a high accuracy but relatively lo.discrimination between sleep and wakefulness.This paper deals with the problem of automatically estimat-,ing a simplified hypnogram (wakefulness, REM, and NREM),and three standard sleep parameters: 1) SE, 2) REM,The sleep parameters are estimated using two different meth-,ods: first, the Hypnogram is estimated from the data and the,sleep parameters computed. PSG 824 points 2. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. [Online]. The chronotypes were assessed in 49573 subjects by the adjusted mid-point of sleep on free days (MSFsc). PSG made an immediate impact, winning promotion to Ligue 1 and claiming the Ligue 2 title in their first season. (CENC)/Faculdade de Medicina da Universidade de Lisboa (FMUL), 1649-028.Lisbon, Portugal (e-mail: teresapaiva0@gmail.com).J. Heart-rate regulation is part of the autonomous nervous system and sympathetic tone is strongly influenced by the sleep stages.IEEE transactions on bio-medical engineering,Social jetlag and chronotypes in the Chinese population measured with wearable devices (Preprint),Automating sleep stage classification using wireless, wearable sensors,A comparison of probabilistic classifiers for sleep stage classification,The research of sleep staging based on single-lead electrocardiogram and deep neural network,Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle Aged Adults,Sleep Tracker and Smartphone: Strengths and Limits to Estimate Sleep and Sleep-Disordered Breathing,Sleep stage classification from heart-rate variability using long short-term memory neural networks,Complexity and Nonlinearities in Cardiorespiratory Signals in Sleep and Sleep Apnea,VCAMS: Viterbi-based Context Aware Mobile Sensing to Trade-off Energy and Delay,An Evaluation of Cardiorespiratory and Movement Features With Respect to Sleep-Stage Classification,Sleep and Wakefulness State Detection in Nocturnal Actigraphy Based on Movement Information,Sleep staging from Heart Rate Variability: time-varying spectral features and Hidden Markov Models,Optimization of Time-Variant Autoregressive Models for tracking REM - Non REM transitions during sleep,Heart Rate Variability: Standards of Measurements, Physiological Interpretation, And Clinical Use,Stress classification by separation of respiratory modulations in heart rate variability using orthogonal subspace projection,Breathing during REM and non-REM sleep: Correlated versus uncorrelated behaviour,Signal Processing Methods for Heart Rate Variability,On the Generalized Distance in Statistics,Biological quantification from imaging - cell-cell adhesion assessment,Quantitative perfusion imaging by multiple-delay ASL MRI.The addition of entropy-based regularity parameters improves sleep stage classification based on hea...AUTOMATIC SLEEP SCORING BASED ONLY ON ELECTROCARDIOGRAM RECORDS.Sleep stage classification of sleep apnea patients using decision-tree-based support vector machines...Dynamics of Heart Rate and Sleep Stages in Normals and Patients with Sleep Apnea. The optimized TVAM was then employed in the analysis of tachograms derived from ECGs recorded during a whole night, through a sensorized T-shirt, from 9 healthy subjects. This strategy consists of a sequential forward selection (SFS) feature selection method and a decision-tree-based support vector machines (DTB-SVM) classifier for discriminating three types of sleep based on electrocardiogram (ECG) signals. A first improvement of TVAM was achieved by choosing the best typology of forgetting factor in the analysis of a tachogram obtained during a sitting-to-standing test; then, a method for improving robustness of AR recursive identification with respect to outliers was selected by analyzing a tachogram with an ectopic beat. Twelve validation studies were identified, evaluating sleep trackers and smartphone app performances compared to polysomnography (PSG) or actigraphy for sleep assessment in healthy and clinical samples. Experimental setup: the subject lies on the bed with the Doppler radar located on top of the subject with a distance of 0.8 m. Meanwhile, the PSG is attached to provide the ground truth signals. The 2010–11 season was French football club Paris Saint-Germain's 38th professional season, their 38th season in Ligue 1 and their 37th consecutive season in French top-flight.PSG was coached by Antoine Kombouaré.The club was presided by Robin Leproux.PSG was present in the Ligue 1, the Coupe de France, the Coupe de la Ligue and the UEFA Europa League. However, effects of respiration are often ignored in studies of heart rate variability. (three class discrimination) leads to a poor Accuracy/Gmean,which are lower than the worst result from T,The three sleep parameters and the estimation error,computed, for each dataset, using the estimated hypnogram and,incorporating the rejection information, the alternative param-,eter estimation outperforms the hypnogram method in all the,Using a RF of 10% and the alternative parameter estimation,method, the average values are almost coincident with the real,values. We also implemented VCAMS on an Android-based device to estimate its computational costs under realistic operational conditions. Rayleigh mixture model for plaque characterization in in-,tion. Methods 3, no. In,movement (REM), and nonREM (NREM) sleep percentages are,automatically estimated from physiological (ECG and respiration),and behavioral (Actigraphy) nocturnal data. RESULTS In this paper, we propose VCAMS: a Viterbi-based Context Aware Mobile Sensing mechanism that adaptively finds an optimized sensing schedule to decide when to trigger the sensors for data collection while trading off the sensing energy and the delay to detect a state change.
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