The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Ahmed A. Morsy - One of the best experts on this subject based on the ideXlab platform.
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EMBC - Consistency of Sleep Restoration Gain (SRG) as a measure for assessing sleep quality
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012Co-Authors: Islam S. Badreldin, Ahmed A. MorsyAbstract:We propose a new sleep quality measure that assesses the sleep restorative gain of a polysomnography sleep record. In this preliminary investigation, we derive this new measure from manually scored sleep Hypnograms. We compare the proposed measure to classical sleep indices such as TST, SE, and ArI, and demonstrate its self-consistency and degree of correlation with these measures. Using 47 sleep records from publicly available sleep databases, we graphically and quantitatively demonstrate the effectiveness of the proposed measure in summarizing the Hypnogram of a sleep record.
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Consistency of Sleep Restoration Gain (SRG) as a measure for assessing sleep quality
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: Islam S. Badreldin, Ahmed A. MorsyAbstract:We propose a new sleep quality measure that assesses the sleep restorative gain of a polysomnography sleep record. In this preliminary investigation, we derive this new measure from manually scored sleep Hypnograms. We compare the proposed measure to classical sleep indices such as TST, SE, and ArI, and demonstrate its self-consistency and degree of correlation with these measures. Using 47 sleep records from publicly available sleep databases, we graphically and quantitatively demonstrate the effectiveness of the proposed measure in summarizing the Hypnogram of a sleep record.
Dean Cvetkovic - One of the best experts on this subject based on the ideXlab platform.
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EMBC - Characterising insomnia: A graph spectral theory approach
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2020Co-Authors: Ramiro Chaparro-vargas, Thomas Penzel, Beena Ahmed, Dean CvetkovicAbstract:This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated Hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort. Our findings demonstrated that the similarity distances based on eigenvalues determination, i.e. d 1 and d 4 were the most reliable and robust measures to characterise insomniacs, discriminating 93% and 87% of the affected population, respectively.
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insomnia characterization from Hypnogram to graph spectral theory
IEEE Transactions on Biomedical Engineering, 2016Co-Authors: Ramiro Chaparrovargas, Thomas Penzel, Beena Ahmed, Niels Wessel, Dean CvetkovicAbstract:Objective: To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms. Methods: The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) Hypnogram generation module that implemented a fuzzy inference system (FIS), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures ( $d_1$ , $d_2$ , $d_3$ , $d_4$ ). The study employed sleep onset periods from 16 control and 16 subjects with insomnia. Results: State-spaced TVARMA processes with recursive particle filtering provided resilience to nonlinear, nonstationary, and non-Gaussian conditions of biosignals. FIS managed automated sleep scoring robust to intersubjects’ and interraters’ variability. The similarity distances quantified in a scalar measure the transitions amongst sleep onset stages, computed from expert and automated Hypnograms. A statistical set of unpaired two-tailed $t$ -tests suggested that distances $d_1$ , $d_2$ , and $d_3$ had larger statistical significance ( $p_{d_1} ) to characterize sleeping patterns. The logistic regression model classified control and subjects with insomnia with sensitivity $87 \%$ , specificity $75 \%$ , and accuracy $81 \%$ . Conclusion: Our approach can perform a supportive role in either biosignal processing, sleep staging, insomnia characterization, or all the previous, coping with time-consuming procedures and massive data volumes of standard protocols. Significance: The introduction of graph spectral theory and logistic regression for the diagnosis of insomnia represents a novel concept, attempting to describe complex sleep dynamics throughout transitions networks and scalar measures.
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Insomnia Characterization: From Hypnogram to Graph Spectral Theory
IEEE Transactions on Biomedical Engineering, 2016Co-Authors: Ramiro Chaparro-vargas, Thomas Penzel, Beena Ahmed, Niels Wessel, Dean CvetkovicAbstract:Objective: To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms. Methods: The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) Hypnogram generation module that implemented a fuzzy inference system (F'S), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures (d1, d2, d3, d4). The study employed sleep onset periods from 16 control and 16 subjects with insomnia. Results: Statespaced TVARMA processes with recursive particle filtering provided resilience to nonlinear, nonstationary, and non-Gaussian conditions of biosignals. F'S managed automated sleep scoring robust to intersubjects' and interraters' variability. The similarity distances quantified in a scalar measure the transitions amongst sleep onset stages, computed from expert and automated Hypnograms. A statistical set of unpaired two-tailed t-tests suggested that distances d1, d2, and d3 had larger statistical significance (pd1
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Classification of healthy subjects and insomniac patients based on automated sleep onset detection
IFMBE Proceedings, 2015Co-Authors: Chamila Dissanayaka, Thomas Penzel, Beena Ahmed, Haslaile Abdullah, Dean CvetkovicAbstract:This work aims to investigate new indexes quantitatively differentiate sleep insomnia patients from healthy subjects, in the context of sleep onset fluctuations. Our study included the use of existing PSG dataset, of 20 healthy subjects and 20 insomniac subjects. The differences between normal sleepers and insomniacs was investigated, in terms of dynamics and content of Sleep Onset (SO) process. An automated system was created to achieve this and it consists of six steps: 1) pre-processing of signals 2) feature extraction 3) classification 4) automatic scoring 5) sleep onset detection 6) identification of subject groups. The pre-processing step consisted of the removal of noise and movement artifacts from the signals. The feature extracting step consists of extracting time, frequency and non-linear features of Electroencephalogram (EEG) and Electromyogram (EMG) signals. In the third step, classification was done using ANN (Artificial Neural Networks) classifier. The fourth step consisted of scoring sleep stages (wake, S1, S2, S3 and REM) and produced a Hypnogram. In the fifth step, we are detecting sleep onset from our automatic detected Hypnogram and identified time of SO reference point and the combination of stages. In the final step we differentiated healthy subjects from insomniac patients based on the parameters calculated in the fifth step.
Rakesh Kumar Sinha - One of the best experts on this subject based on the ideXlab platform.
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eeg power spectrum and neural network based sleep Hypnogram analysis for a model of heat stress
Journal of Clinical Monitoring and Computing, 2008Co-Authors: Rakesh Kumar SinhaAbstract:Objective An effective application of back- propagation artificial neural network (ANN) in preparation of sleep-Hypnogram based on electroencephalogram (EEG) power spectra under acute as well as chronic heat stress has been presented.
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EEG power spectrum and neural network based sleep-Hypnogram analysis for a model of heat stress
Journal of Clinical Monitoring and Computing, 2008Co-Authors: Rakesh Kumar SinhaAbstract:Objective An effective application of back- propagation artificial neural network (ANN) in preparation of sleep-Hypnogram based on electroencephalogram (EEG) power spectra under acute as well as chronic heat stress has been presented. Methods Rats were divided in three groups (i) acute heat stress—subjected to a single exposure for four hours at 38°C; (ii) chronic heat stress—exposed for 21 days daily for one hour at 38°C, and (iii) handling control groups. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation of power spectra, training and testing of ANN. Results The power spectrum analyses of EEG show that changes in higher frequency components (β_2) were significant in all sleep-wake states following both acute and chronic heat stress conditions. The power of β_2 activity after acute heat exposure was significantly decreased during SWS (slow wave sleep) ( P < 0.05) and REM (rapid eye movement) sleep ( P < 0.05), while reverse was observed in AWA (awake state) ( P < 0.05). Following chronic heat exposure, β_2 activity was found increased in all three sleep-wake stages ( P < 0.05). The ANN used for sleep-Hypnogram preparation contains 64 nodes in input layer, weighted from power spectrum data from 0 to 32 Hz, 14 nodes in hidden layer and 3 output nodes. The results obtained from the study, suggest increased sleep efficiency following acute exposure to heat stress while fragmented sleep with decreased sleep efficiency following chronic heat stress. Conclusion The ANN can be used for the analysis of stressful events by calculating the sleep-EEG alterations.
Islam S. Badreldin - One of the best experts on this subject based on the ideXlab platform.
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EMBC - Consistency of Sleep Restoration Gain (SRG) as a measure for assessing sleep quality
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012Co-Authors: Islam S. Badreldin, Ahmed A. MorsyAbstract:We propose a new sleep quality measure that assesses the sleep restorative gain of a polysomnography sleep record. In this preliminary investigation, we derive this new measure from manually scored sleep Hypnograms. We compare the proposed measure to classical sleep indices such as TST, SE, and ArI, and demonstrate its self-consistency and degree of correlation with these measures. Using 47 sleep records from publicly available sleep databases, we graphically and quantitatively demonstrate the effectiveness of the proposed measure in summarizing the Hypnogram of a sleep record.
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Consistency of Sleep Restoration Gain (SRG) as a measure for assessing sleep quality
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: Islam S. Badreldin, Ahmed A. MorsyAbstract:We propose a new sleep quality measure that assesses the sleep restorative gain of a polysomnography sleep record. In this preliminary investigation, we derive this new measure from manually scored sleep Hypnograms. We compare the proposed measure to classical sleep indices such as TST, SE, and ArI, and demonstrate its self-consistency and degree of correlation with these measures. Using 47 sleep records from publicly available sleep databases, we graphically and quantitatively demonstrate the effectiveness of the proposed measure in summarizing the Hypnogram of a sleep record.
Naresh M. Punjabi - One of the best experts on this subject based on the ideXlab platform.
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MODELING SLEEP FRAGMENTATION IN POPULATIONS OF SLEEP HypnogramS
2020Co-Authors: Bruce J. Swihart, Naresh M. Punjabi, Ciprian M. CrainiceanuAbstract:We introduce methods for the analysis of large populations of sleep architectures (Hypnograms) that respect the 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. By applying these methods to the Hypnograms of 5598 subjects from the Sleep Heart Health Study we: 1) provide the first analysis of sleep Hypnogram data of such size and complexity in a community cohort with a 4-level comorbidity; 2) compare 5-state 20-transition-type sleep to 3-state 6-transition-type sleep for a check of feasibility and information-loss; 3) extend current approaches to multivariate survival data analysis to populations of time-to-transition processes; and 4) provide scalable solutions for data analyses required by the case study. This allows us to provide detailed new insights into the association between sleep apnea and sleep architecture. Supporting R as well as SAS code and data are included in the online supplementary materials.
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Modeling populations of sleep Hypnograms
2020Co-Authors: Bruce J. Swihart, Naresh M. Punjabi, Ciprian M. CrainiceanuAbstract:We introduce methods for the analysis of large populations of sleep architectures (Hypnograms) that respect the 5-state structure defined by the American Academy of Sleep Medicine. By applying these methods to the Hypnograms of 5598 subjects from the Sleep Heart Health Study we: 1) provide an unprecedented high resolution view of the sleep architecture landscape in a community cohort; 2) extend current approaches to multivariate survival data analysis to populations of time-to-transition processes; and 3) provide scalable solutions for data analyses required by the case study. This allows us to provide detailed new insights into the association between sleep apnea and sleep architecture. Supporting R as well as SAS code and data are included in the online supplementary materials.
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Modeling sleep fragmentation in sleep Hypnograms: An instance of fast, scalable discrete-state, discrete-time analyses
Computational Statistics & Data Analysis, 2015Co-Authors: Bruce J. Swihart, Naresh M. Punjabi, Ciprian M. CrainiceanuAbstract:Methods are introduced for the analysis of large sets of sleep study data (Hypnograms) using a 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. Application of these methods to the Hypnograms of 5598 subjects from the Sleep Heart Health Study provide: the first analysis of sleep Hypnogram data of such size and complexity in a community cohort with a range of sleep-disordered breathing severity; introduce a novel approach to compare 5-state (20-transition-type) to 3-state (6-transition-type) sleep structures to assess information loss from combining sleep state categories; extend current approaches of multivariate survival data analysis to clustered, recurrent event discrete-state discrete-time processes; and provide scalable solutions for data analyses required by the case study. The analysis provides detailed new insights into the association between sleep-disordered breathing and sleep architecture. The example data and both R and SAS code are included in online supplementary materials.
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mixed effect poisson log linear models for clinical and epidemiological sleep Hypnogram data
Statistics in Medicine, 2012Co-Authors: Bruce J. Swihart, Ciprian M. Crainiceanu, Brian S Caffo, Naresh M. PunjabiAbstract:Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis.
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Mixed effect Poisson log‐linear models for clinical and epidemiological sleep Hypnogram data
Statistics in Medicine, 2012Co-Authors: Bruce J. Swihart, Ciprian M. Crainiceanu, Brian S Caffo, Naresh M. PunjabiAbstract:Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis.