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

Oxyhemoglobin Saturation Overshoot Following Obstructive Breathing Events Mitigates Sleep Apnea-Induced Glucose Elevations

Background: Obstructive sleep apnea (OSA) and nocturnal hypoxia are associated with disturbances in glucose regulation and diabetes. Temporal associations between OSA, oxygenation profiles and glucose have not been well-described. We hypothesized that oxyhemoglobin desaturation during apneic events and subsequent post-apnea saturation overshoot predict nocturnal glucose.Methods: In 30 OSA patients who underwent polysomnography while subjected to CPAP withdrawal, we characterized SPO2 swings by frequency, desaturation depth, and overshoot height relative to baseline. We examined the associations between frequently sampled glucose and SPO2 swings during the preceding 10 min. We developed multi-variable mixed effects linear regression to examine the independent associations between glucose and each level of these SPO2 swings, while controlling for OSA severity.Results: Desaturation depth was not associated with glucose (p > 0.05). In contrast, overshoot was associated with glucose in a dose-dependent manner. Each SPO2 peak that did not rise to within 1% of baseline was associated with incremental glucose elevations of 0.49 mg/dL (p = 0.01), whereas peaks that exceeded baseline by >1% were associated with glucose reductions of 0.46 mg/dL. Overshoot remained an independent predictor of glucose after adjustment for mean SPO2 and OSA severity (p > 0.05).Conclusions: Vigorous SPO2 improvements after apneic events may protect patients against OSA-related glucose elevations

Brain Tumour Classification Using Deep Features from Structural MRI

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INTERMITTENT HYPOXEMIA IN PRETERM INFANTS

Intermittent hypoxemia (IH) is defined as episodic drops in oxygen saturation (SpO2). Virtually all preterm infants have IH events. Extremely preterm infants have hundreds of IH events per day. The extent of IH is not apparent clinically as accurately documenting cardiorespiratory events for day-to-day patient care management is challenging. High resolution pulse oximeters with 2 second averaging time are currently the ideal methods to measure IH. We have developed novel methods and processes to accurately and efficiently calculate an IH profile that reflects to spectrum of the problem. The natural progression of IH is dynamic. There is low incidence of IH in the few 2 weeks of life, followed by a progressive increase until peak IH at 4-5 week after which IH plateaus. Multiple factors place preterm infants at high risk for increased IH. These factors include respiratory immaturity, lung disease, and anemia. We also show that preterm infants prenatally exposed to opioids or inflammation (due to maternal chorioamnionitis) have increased IH measures compared to unexposed infants. Interestingly, the increased IH in the exposed groups persists beyond the immediate postnatal period. Brief episodes of oxygen desaturations may seem clinically insignificant; however, these events may have a cumulative effect on neonatal outcomes. There is mounting evidence from both animal models and clinical studies suggesting that IH is associated with injury and poor outcomes such as impaired growth, retinopathy of prematurity and neurodevelopmental impairment. In addition data from neonatal animal models and adults with obstructive sleep apnea suggest that IH is pro inflammatory itself. We demonstrate in this document for the first time in preterm infants that IH is associated with increased serum inflammatory marker, C-reactive protein. Finally, a valuable experience throughout this process is working with a talented and dedicated multidisciplinary team. We are a solid example of the value of team science during this new era of clinical and translational research. Our respiratory control research program is one of handful programs nationwide able to perform such high-fidelity studies related to cardiorespiratory events in preterm infants. We will continue to tackle complex questions involving health of infants

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

The role of the central chemoreceptor in causing periodic breathing.

In a previous publication (Fowler et aL, 1993), we reduced the classical cardiorespiratory control model of (Grodins et aL, 1967) to a much simpler form, which we then used to study the phenomenon of periodic breathing. In particular, cardiac output was assumed constant, and a single (constant) delay representing arterial blood transport time between lung and brain was included in the model. In this paper we extend this earlier work, both by allowing for the variability in transport delays, due to the dependence of cardiac output on blood gas concentrations, and also by including further delays in the system. In addition, we extensively discuss the physiological implications of parameter variations in the model; several novel mechanisms for periodic breathing in clinical situations are proposed. The results are discussed in the light of recent observational studies. Keywords: Periodic breathing; Cheyne-Stokes respiration; heart-rate variability*, differential-delay equations. 1

Dynamical models for neonatal intensive care monitoring

The vital signs monitoring data of an infant receiving intensive care are a rich source of information about its health condition. One major concern about the state of health of such patients is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle and current diagnosis procedures involve slow laboratory testing, sepsis detection based on the monitored physiological dynamics is a clinically significant task. This challenging problem can be thoroughly modelled as real-time inference within a machine learning framework. In this thesis, we develop probabilistic dynamical models centred around the goal of providing useful predictions about the onset of neonatal sepsis. This research is characterised by the careful incorporation of domain knowledge for the purpose of extracting the infant’s true physiology from the monitoring data. We make two main contributions. The first one is the formulation of sepsis detection as learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detection of neonatal sepsis. In addition, the proposed approach involves exact marginalisation over missing data at inference time. When applying the ARHMM on a real-world dataset, we found that it can produce effective predictions about the onset of sepsis. Second, both sepsis and clinical event detection are formulated as learning and inference in a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical systems where complex interactions between modes of operation can be represented as a twolevel hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis. The model parameterisation and estimation procedures are adapted to the specifics of physiological monitoring data. We demonstrate that the performance of the HSLDS for the detection of sepsis is not statistically different from the AR-HMM, despite the fact that the latter model is given “ground truth” annotations of the patient’s physiology

Central Sleep Apnea Is Associated with an Abnormal P-Wave Terminal Force in Lead V1 in Patients with Acute Myocardial Infarction Independent from Ventricular Function

Sleep-disordered breathing (SDB) is highly prevalent in patients with cardiovascular disease. We have recently shown that an elevation of the electrocardiographic (ECG) parameter P wave terminal force in lead V1 (PTFV1) is linked to atrial proarrhythmic activity by stimulation of reactive oxygen species (ROS)-dependent pathways. Since SDB leads to increased ROS generation, we aimed to investigate the relationship between SDB-related hypoxia and PTFV1 in patients with first-time acute myocardial infarction (AMI). We examined 56 patients with first-time AMI. PTFV1 was analyzed in 12-lead ECGs and defined as abnormal when ≥4000 µV*ms. Polysomnography (PSG) to assess SDB was performed within 3–5 days after AMI. SDB was defined by an apnea-hypopnea-index (AHI) >15/h. The multivariable regression analysis showed a significant association between SDB-related hypoxia and the magnitude of PTFV1 independent from other relevant clinical co-factors. Interestingly, this association was mainly driven by central but not obstructive apnea events. Additionally, abnormal PTFV1 was associated with SDB severity (as measured by AHI, B 21.495; CI [10.872 to 32.118]; p < 0.001), suggesting that ECG may help identify patients suitable for SDB screening. Hypoxia as a consequence of central sleep apnea may result in atrial electrical remodeling measured by abnormal PTFV1 in patients with first-time AMI independent of ventricular function. The PTFV1 may be used as a clinical marker for increased SDB risk in cardiovascular patients