Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness

Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and wakefulness in 1 second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs, the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human expert technicians, the MAD significantly outperformed the average human scorer for arousal detection with a difference in F1 score of 0.09. After controlling for other known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds ($\beta$ = -0.67, p = 0.0075). The MAD outperformed the average human expert and the MAD-predicted arousals were shown to be significant predictors of MSL, which demonstrate clinical validity the MAD.Comment: 40 pages, 13 figures, 9 table

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

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

OBJECTIVE: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. METHODS: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. RESULTS: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. CONCLUSIONS: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination

Diagnosis of sleep apnoea using a mandibular monitor and machine learning analysis: one-night agreement compared to in-home polysomnography

Background: The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG. Methods: 40 suspected OSA patients underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15, and 30 events/hour). Results: 31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m2). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI −23.25 to + 9.73 events/hour). However, for 15 patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI 5–15: MM-ORDI overestimation + 3.70 (95% CI −0.53 to + 18.32) events/hour). In 16 patients with moderate-severe OSA (n = 9 with PSG-ORDI 15–30 events/h and n = 7 with a PSG-ORD > 30 events/h), there was an underestimation (PSG-ORDI > 15: MM-ORDI underestimation −8.70 (95% CI −28.46 to + 4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively. Conclusion: The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients’ own home. Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT0426255

Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications

Assessment of the Physiological Network in Sleep Apnea

Objective: Machine Learning models, in particular Artificial Neural Networks, have shown to be applicable in clinical research for tumor detection and sleep phase classification. Applications in systems medicine and biology, for example in Physiological Networks, could benefit from the ability of these methods to recognize patterns in high-dimensional data, but decisions of an Artificial Neural Network cannot be interpreted based on the model itself. In a medical context this is an undesirable characteristic, because hidden age, gender or other data biases negatively impact the model quality. If insights are based on a biased model, the ability of an independent study to come to similar conclusions is limited and therefore an essential property of scientific experiments, known as results reproducibility, is violated. Besides results reproducibility, methods reproducibility allows others to reproduce exact outputs of computational experiments, but requires data, code and runtime environments to be available. These challenges in interpretability and reproducibility are addressed as part of an assessment of the Physiological Network in Obstructive Sleep Apnea. Approach: A research platform is developed, that connects medical data, code and environ-ments to enable methods reproducibility. The platform employs a compute cluster or cloud to accelerate the demanding model training. Artificial Neural Networks are trained on the Physiological Network data of a healthy control group for age and gender prediction to verify the influence of these biases. In a subsequent study, an Artificial Neural Network is trained to classify the Physiological Networks in Obstructive Sleep Apnea and a healthy control group. The state-of-the-art interpretation method DeepLift is applied to explain model predictions. Results: An existing collaboration platform has been extended for sleep research data and modern container technologies are used to distribute training environments in compute clusters. Artificial Neural Network models predict the age of healthy subjects in a resolution of one decade and correctly classify the gender with 91% accuracy. Due to the verified biases, a matched dataset is created for the classification of Obstructive Sleep Apnea. The classification accuracy reaches 87% and DeepLift provides biomarkers as significant indicators towards or against the disorder. Analysis of misclassified samples shows potential Obstructive Sleep Apnea phenotypes. Significance: The presented platform is extensible for future use cases and focuses on the reproducibility of computational experiments, a concern across many disciplines. Machine learning approaches solve analysis tasks on high-dimensional data and novel interpretation techniques provide the required transparency for medical applications.Ziel: Methoden des maschinellen Lernens, insbesondere künstliche neuronale Netze, finden Anwendung in der klinischen Forschung, um beispielsweise Tumorzellen oder Schlafphasen zu klassifizieren. Anwendungen in der Systemmedizin und -biologie, wie physiologische Netzwerke, könnten von der Fähigkeit dieser Methoden, Muster in großen Merkmalsräumen zu finden, profitieren. Allerdings sind Entscheidungen eines künstlichen neuronalen Netzes nicht allein anhand des Modells interpretierbar. In einem medizinischen Kontext ist dies eine unerwünschte Charakteristik, weil die Daten, mit denen ein Modell trainiert wird, versteckte Einflüsse wie Alters- und Geschlechtsabhängigkeiten beinhalten können. Erkenntnisse, die auf einem beeinflussten Modell basieren, sind nur bedingt durch unabhängige Studien nach-vollziehbar, sodass keine Ergebnisreproduzierbarkeit gegeben ist. Neben der Ergebnisreproduzier-barkeit bezeichnet Methodenreproduzierbarkeit die Möglichkeit exakte Programmausgaben zu reproduzieren, was die Verfügbarkeit von Daten, Programmcode und Ausführungsumgebungen voraussetzt. Diese Promotion untersucht Veränderungen im physiologischen Netzwerk bei obstruktivem Schlafapnoesyndrom mit Methoden des maschinellen Lernens und adressiert dabei die genannten Herausforderungen der Interpretierbarkeit und Reproduzierbarkeit. Ansatz: Es wird eine Forschungsplattform entwickelt, die medizinische Daten, Programmcode und Ausführungsumgebungen verknüpft und damit Methodenreproduzierbarkeit ermöglicht. Die Plattform bindet zur Beschleunigung des ressourcenintensiven Modelltrainings verteilte Rechenressourcen in Form eines Clusters oder einer Cloud an. Künstliche neuronale Netze werden zur Bestimmung des Alters und des Geschlechts anhand der physiologischen Daten einer gesunden Kontrollgruppe trainiert, um den Einfluss der Alters- und Geschlechtsabhängigkeiten zu untersuchen. In einer Folgestudie werden die Unterschiede im physiologischen Netzwerk einer Gruppe mit obstruktivem Schlafapnoesyndrom und einer gesunden Kontrollgruppe klassifiziert. DeepLift, eine Interpretationsmethode nach aktuellem Stand der Technik, wird zur Erklärung der Modellvorhersagen angewendet. Ergebnisse: Eine existierende Forschungsplattform wurde für die Verarbeitung schlafbezogener Forschungsdaten erweitert und Containertechnologien ermöglichen die Bereitstellung der Ausführungsumgebung eines Experiments in einem Cluster. Künstliche neuronale Netze können anhand der physiologischen Daten das Alter einer Person bis auf eine Dekade genau bestimmen und eine Geschlechtsklassifikation erreicht eine Genauigkeit von 91%. Die Ergebnisse bestätigen den Einfluss der Alters- und Geschlechtsabhängigkeiten, sodass für Schlafapnoeklassifikationen zunächst eine Datenbasis geschaffen wird, in der die Geschlechts- und Altersverteilung zwischen gesunden und kranken Gruppen ausgeglichen ist. Die resultierenden Modelle erreichen eine Klassifikationsgenauigkeit von 87%. DeepLift weist auf Biomarker und mögliche physiologische Schlafapnoe-Phänotypen im Tiefschlaf hin. Signifikanz: Die vorgestellte Plattform ist für zukünftige Anwendungsfälle erweiterbar und ermöglicht Methodenreproduzierbarkeit, was über den Einsatz in der Medizin hinaus auch in anderen Disziplinen von Bedeutung ist. Maschinelles Lernen bietet sinnvolle Ansätze für die Analyse hochdimensionaler Daten und neue Interpretationstechniken schaffen die notwendige Transparenz für medizinische Anwendungszwecke

A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals

Significant interest exists in the potential to use continuous physiological monitoring to prevent respiratory complications and death, especially in the postoperative period. Smart alarm-threshold based systems are currently used with hospitalized patients. Despite clinical observations and research studies to support benefit from these systems, several concerns remain. For example, a small difference in a threshold may significantly increase the alarm rate. A significant increase in alarm related adverse outcomes has been reported by health care oversight organizations. Also, it has been recently shown that the signaled alarms are indeed late detections for clinical instability leading to a delayed recognition and less successful clinical intervention. This dissertation advances the state of art by moving from just monitoring towards prediction of physiological variables. Moving in this direction introduces research challenges in many aspects. Although existing literature describes many metrics for characterizing the prediction performance of time series models, these metrics may not be relevant for physiological signals. In these signals, clinicians are often concerned about specific regions of clinical interest. This dissertation develops and implements different types of metrics that can characterize the performance in predicting clinically relevant regions in physiological signals. In the era of massive data, biomedical devices are able to collect a large number of synchronized physiological signals recording a significant time history of a patient's physiological state. Directionality between physiological signals and which ones can be used to improve the ability to predict the other ones is an important research question. This dissertation uses a dynamic systems perspective to address this question. Metrics are also defined to characterize the improvement achieved by incorporating additional data into the prediction model of a physiological signal of interest. Although a rich literature exists on time series prediction models, these models traditionally consider the (absolute or square) error between the predicted and actual time series as an objective for optimization. This dissertation proposes two modeling frameworks for predicting clinical regions of interest in physiological signals. The physiological definition of the clinically relevant regions is incorporated in the model development and used to optimize models with respect to predictions of these regions.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116666/1/elmoaqet_1.pd