Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure

Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

The Impact of Cortical State on Neural Coding and Behavior

The brain is never truly silent – up to 80% of its energy budget is expended during ongoing activity in the absence of sensory input. Previous research has shown that sensory neurons are not exclusively influenced by external stimuli but rather reflect interactions between sensory inputs and the ongoing activity of the brain. Yet, whether fluctuations in the state of cortical networks influence sensory coding in neural circuits and the behavior of the animal are unknown. To shed light on this issue, we conducted multi-unit electrophysiology experiments in visual areas V1 and V4 of behaving monkeys. First, we studied the impact of neural population spiking before stimulus presentation on orientation discrimination in the primary visual cortex. We found that when neuronal populations are in a low firing state, they have a higher capacity to discriminate stimulus features despite an overall reduction in evoked responses. Importantly, behavioral performance was significantly improved in the low firing network state. Next, we conducted recordings in the visual cortical area V4 while animals participated in a natural image orientation discrimination task to determine whether fluctuations in local population synchrony during wakefulness play any role in modulating network and behavioral performance. We found that populations of cells exhibit rapid fluctuations in synchrony of ongoing activity ranging from desynchronized responses, indicative of high alertness, to more synchronized responses, indicative of drowsiness. These state fluctuations control the variability in the accuracy of population coding and behavioral performance across trials in a visual discrimination task. When the local population activity is desynchronized, the correlated variability between neurons is reduced, and network and behavioral performance are improved. Lastly, we controlled the state of cortical networks by manipulating the animal’s behavioral state from wakefulness to rest. Thus, we analyzed population recordings from area V4 while the animals participated in an orientation discrimination task, which was immediately followed by a brief resting period of 20-30 minutes, and lastly, by a second task period (Task – Rest – Task). We found that cortical networks were desynchronized after rest such that behavioral performance was improved relative to the pre-rest condition. Altogether, the findings in this thesis demonstrate that the variability in spontaneous cortical activity is not simply noise but rather contains a dynamic structure which controls how incoming sensory information is optimally integrated with ongoing processes to guide network coding and behavior

Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability

Producción CientíficaHeart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-minute segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1–5 e/s; 5–10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.Ministerio de Ciencia, Innovación y Universidades y el Fondo Europeo de Desarrollo Regional (FEDER) under projects (PID2019-104881RB-I00), (PID2020-115468RB-I00), (PDC2021-120775-I00) and (PID2021-126734OB-C21)Sociedad Española de Sueño (SES) en el marco del proyecto “Beca de Investigación SES 2019”, by 'CIBER-Consorcio Centro de Investigación Biomédica en Red- (CB19/01/00012)' a través del 'Instituto de Salud Carlos III' co- financiado con fondos FEDER, así como bajo el proyecto SleepyHeart de la convocatoria de valorización 2020, y por el Gobierno de Aragón (Grupo de Referencia BSICoS T39-20R).The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002).Ministerio de Ciencia, Innovación y Universidades, “Ayudas para contratos predoctorales para la Formación de Doctores” grant (PRE2018-085219) and “Ramón y Cajal” (MICIU/FSE) grant (RYC2019-028566-I)Institutos Nacionales de Salud-"Ensayo de adenoamigdalectomía infantil (CHAT)"- (HL083075, HL083129, UL1-RR-024134, UL1 RR024989) and (grant AG061824)

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Prestimulus vigilance predicts response speed in an easy visual discrimination task

<p>Abstract</p> <p>Background</p> <p>Healthy adults show considerable within-subject variation of reaction time (RT) when performing cognitive tests. So far, the neurophysiological correlates of these inconsistencies have not yet been investigated sufficiently. In particular, studies rarely have focused on alterations of prestimulus EEG-vigilance as a factor which possibly influences the outcome of cognitive tests. We hypothesised that a low EEG-vigilance state immediately before a reaction task would entail a longer RT. Shorter RTs were expected for a high EEG-vigilance state.</p> <p>Methods</p> <p>24 female students performed an easy visual discrimination task while an electroencephalogram (EEG) was recorded. The vigilance stages of 1-sec-EEG-segments before stimulus presentation were classified automatically using the computer-based Vigilance Algorithm Leipzig (VIGALL). The mean RTs of each EEG-vigilance stage were calculated for each subject. A paired t-test for the EEG-vigilance main stage analysis (A vs. B) and a variance analysis for repeated measures for the EEG-vigilance sub-stage analysis (A1, A2, A3, B1, B2/3) were calculated.</p> <p>Results</p> <p>Individual mean RT was significantly shorter for events following the high EEG-vigilance stage A compared to the lower EEG-vigilance stage B. The main effect of the sub-stage analysis was marginal significant. A trend of gradually increasing RT was observable within the EEG-vigilance stage A.</p> <p>Conclusion</p> <p>We conclude that an automatically classified low EEG-vigilance level is associated with an increased RT. Thus, intra-individual variances in cognitive test might be explainable in parts by the individual state of EEG-vigilance. Therefore, the accuracy of neuro-cognitive investigations might be improvable by simultaneously controlling for vigilance shifts using the EEG and VIGALL.</p

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976