Correlation and real time classification of physiological streams for critical care monitoring.

This thesis presents a framework for the deployment of algorithms that support the correlation and real-time classification of physiological data streams through the development of clinically meaningful alerts using a blend of expert knowledge in the domain and pattern recognition programming based on clinical rules. Its relevance is demonstrated via a real world case study within the context of neonatal intensive care to provide real-time classification of neonatal spells. Events are first detected in individual streams independently; then synced together based on timestamps; and finally assessed to determine the start and end of a multi-signal episode. The episode is then processed through a classifier based on clinical rules to determine a classification. The output of the algorithms has been shown, in a single use case study with 24 hours of patient data, to detect clinically significant relative changes in heart rate, blood oxygen saturation levels and pauses in breathing in the respiratory impedance signal. The accuracy of the algorithm for detecting these is 97.8%, 98.3% and 98.9% respectively. The accuracy for correlating the streams and determining spells classifications is 98.9%. Future research will focus on the clinical validation of these algorithms and the application of the framework for the detection and classification of signals in other clinical contexts

A flexible, longitudinal and surrogate consent model: Consent of Infants for Neonatal Secondary-use research (CoINS) Model

Documenting healthcare, along with technology enabling capture of streaming patient telemetry, can deliver large datasets offering opportunities to discover new insights primarily identified through retrospective secondary use research. Research involving health data requires consent of the subject patient or someone with the power to speak on that patient???s behalf. Flexible consent models that capture consent preferences while allowing updates as preferences change are needed. This research proposes and demonstrates one solution in a case study collecting surrogate consent from parents for the physiological data of infant inpatients in the Neonatal Intensive Care Unit (NICU) and attaching this consent as a wrapper controlling access to their data. 145 parents were approached and 134 provided consent: with 78 percent of infants consented during their first week of life. This research supports the contention that using a flexible consent approach enhances willingness to consent use of infant???s health data for secondary research purposes

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Integrated out-of-hours care arrangements in England: observational study of progress towards single call access via NHS Direct and impact on the wider health system

Objectives: To assess the extent of service integration achieved within general practice cooperatives and NHS Direct sites participating in the Department of Health’s national “Exemplar Programme” for single call access to out-of-hours care via NHS Direct. To assess the impact of integrated out-of-hours care arrangements upon general practice cooperatives and the wider health system (use of emergency departments, 999 ambulance services, and minor injuries units). Design: Observational before and after study of demand, activity, and trends in the use of other health services. Setting: Thirty four English general practice cooperatives with NHS Direct partners (“exemplars”) of which four acted as “case exemplars”. Also 10 control cooperatives for comparison. Main Outcome Measures: Extent of integration achieved (defined as the proportion of hours and the proportion of general practice patients covered by integrated arrangements), patterns of general practice cooperative demand and activity and trends in use of the wider health system in the first year. Results: Of 31 distinct exemplars 21 (68%) integrated all out-of-hours call management by March 2004. Nine (29%) established single call access for all patients. In the only case exemplar where direct comparison was possible, cooperative nurse telephone triage before integration completed a higher proportion of calls with telephone advice than did NHS Direct afterwards (39% v 30%; p<0.0001). The proportion of calls completed by NHS Direct telephone advice at other sites was lower. There is evidence for transfer of demand from case exemplars to 999 ambulance services. A downturn in overall demand for care seen in two case exemplars was also seen in control sites. Conclusion: The new model of out-of-hours care was implemented in a variety of settings across England by new partnerships between general practice cooperatives and NHS Direct. Single call access was not widely implemented and most patients needed to make at least two telephone calls to contact the service. In the first year, integration may have produced some reduction in total demand, but this may have been accompanied by shifts from one part of the local health system to another. NHS Direct demonstrated capability in handling calls but may not currently have sufficient capacity to support national implementation

2017 EURēCA Abstract Book

Listing of student participant abstracts

Extraction of Heart Rate from Multimodal Video Streams of Neonates using Methods of Machine Learning

The World Health Organization estimates that more than one-tenth of births are premature. Premature births are linked to an increase of the mortality risk, when compared with full-term infants. In fact, preterm birth complications are the leading cause of perinatal mortality. These complications range from respiratory distress to cardiovascular disorders. Vital signs changes are often prior to these major complications, therefore it is crucial to perform continuous monitoring of this signals. Heart rate monitoring is particularly important. Nowadays, the standard method to monitor this vital sign requires adhesive electrodes or sensors that are attached to the infant. This contact-based methods can damage the skin of the infant, possibly leading to infections. Within this context, there is a need to evolve to remote heart rate monitoring methods. This thesis introduces a new method for region of interest selection to improve remote heart rate monitoring in neonatology through Photoplethysmography Imaging. The heart rate assessment is based on the standard photoplethysmography principle, which makes use of the subtle fluctuations of visible or infrared light that is reflected from the skin surface within the cardiac cycle. A camera is used, instead of the contact-based sensors. Specifically, this thesis presents an alternative method to manual region of interest selection using methods of Machine Learning, aiming to improve the robustness of Photoplethysmography Imaging. This method comprises a highly efficient Fully Convolutional Neural Network to select six different body regions, within each video frame. The developed neural network was built upon a ResNet network and a custom upsampling network. Additionally, a new post-processing method was developed to refine the body segmentation results, using a sequence of morphological operations and centre of mass analysis. The developed region of interest selection method was validated with clinical data, demonstrating a good agreement (78%) between the estimated heart rate and the reference