Point process models for novelty detection on spatial point patterns and their extremes

Novelty detection is a particular example of pattern recognition identifying patterns that departure from some model of "normal behaviour". The classification of point patterns is considered that are defined as sets of N observations of a multivariate random variable X and where the value N follows a discrete stochastic distribution. The use of point process models is introduced that allow us to describe the length N as well as the geometrical configuration in data space of such patterns. It is shown that such infinite dimensional study can be translated into a one-dimensional study that is analytically tractable for a multivariate Gaussian distribution. Moreover, for other multivariate distributions, an analytic approximation is obtained, by the use of extreme value theory, to model point patterns that occur in low-density regions as defined by X. The proposed models are demonstrated on synthetic and real-world data sets

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available

An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring.

Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data.This work was supported by a UK Engineering and Physical Sciences Research Council (EPSRC) Impact Acceleration Award awarded to PHC; the EPSRC [EP/H019944/1]; the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]; the Oxford and King’s College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grants [WT88877/Z/09/Z] and [WT088641/Z/09/Z]; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s & St Thomas’ NHS Foundation Trust and King’s College London; the NIHR Oxford Biomedical Research Centre Programme; a Royal Academy of Engineering Research Fellowship (RAEng) awarded to DAC; and EPSRC grants EP/P009824/1 and EP/N020774/1 to DAC

Modelling physiological deterioration in post-operative patient vital-sign data

Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events

Predicting Clinical Deteriorations using Wearable Sensors

Introduction Acutely-ill hospitalised patients are at risk of clinical deteriorations such as cardiac arrest, admission to intensive care, or unexpected death. Currently, patients are manually assessed every 4-6 hours to determine the likelihood of subsequent deterioration. However, this is limited to intermittent assessments, delaying time-sensitive interventions. Wearable sensors, combined with an alerting system, could provide continuous automated assessments of the likelihood of deteriorations. To be suitable for hospital use, wearable sensors must be unobtrusive and provide reliable measurements of key vital signs including breathing rate (BR), a key predictor of deteriorations. The aims of this work were: (i) to develop a technique for monitoring BR unobtrusively using wearable sensors, and (ii) to assess whether wearable sensors provide reliable predictions of deteriorations when using this technique. Monitoring breathing rate (BR) unobtrusively Current methods for monitoring BR using wearable sensors are obtrusive. An alternative approach is to estimate BR from electrocardiogram or pulse oximeter signals, which are already acquired by wearable sensors to monitor heart rate and blood oxygen levels. Both signals are subtly modulated by breathing, providing opportunity to use them to monitor BR. I assessed the performance of previously proposed signal processing techniques for estimating BR from these signals in both healthy and hospitalised subjects. Although some techniques were precise enough for use with healthy subjects in the laboratory, they were imprecise when used with hospital patients. Therefore, I developed a novel technique, combining the strengths of time- and frequency-domain techniques. Its performance was assessed on data from 264 subjects. In hospital patients, the technique provided highly precise BRs 86% of the time, which exceeds the performance of manual observation, the current clinical standard. Assessing the reliability of wearable sensors for predicting deteriorations I implemented methods for rejecting unreliable sensor data, and for fusing continuous multiparametric data, to predict deteriorations. These were used alongside the novel technique for monitoring BR to predict deteriorations using wearable sensors. The system was assessed in a clinical trial of 184 hospital patients, conducted in collaboration with clinicians. The reliability of the system was assessed by comparing its predictions against documented deteriorations. Its predictive value was similar to that of the routine manual assessments (AUROCs of 0.78 vs 0.79). Crucially it provided continuous assessment, potentially providing predictions of deteriorations hours earlier than routine practice. Conclusion This work has demonstrated the potential for wearable sensors to reliably and unobtrusively predict deteriorations, when coupled with a novel technique for monitoring BR. This could improve patient outcomes, and reduce costs. Further work should investigate which patients would benefit most from this technology, and whether it could reduce clinical workload. In the future the technology could potentially be used with consumer wearables to improve patient safety in the community, where clinical expertise is less readily available.This poster was displayed at the STEM for Britain event, held in the Houses of Parliament (London, UK) on 12th March 2018

Respiratory rate monitoring to detect deteriorations using wearable sensors

This poster provides an overview of the work described in: P. H. Charlton, "Continuous respiratory rate monitoring to detect clinical deteriorations using wearable sensors," Ph.D. Thesis, King’s College London, 2017.This poster was first presented at the Bioengenuity Keynotes Conference, held on Monday 6th March at the University of Oxford

Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters.

GOAL: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. METHODS: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. RESULTS: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25[Formula: see text]-75[Formula: see text] percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). CONCLUSION: Increased robustness of RR estimation by the proposed method was demonstrated. SIGNIFICANCE: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice

Implementing a system for the real-time risk assessment of patients considered for intensive care

BACKGROUND: Delay in identifying deterioration in hospitalised patients is associated with delayed admission to an intensive care unit (ICU) and poor outcomes. For the HAVEN project (HICF ref.: HICF-R9-524), we have developed a mathematical model that identifies deterioration in hospitalised patients in real time and facilitates the intervention of an ICU outreach team. This paper describes the system that has been designed to implement the model. We have used innovative technologies such as Portable Format for Analytics (PFA) and Open Services Gateway initiative (OSGi) to define the predictive statistical model and implement the system respectively for greater configurability, reliability, and availability. RESULTS: The HAVEN system has been deployed as part of a research project in the Oxford University Hospitals NHS Foundation Trust. The system has so far processed &gt;&#x2009;164,000 vital signs observations and&#x2009;&gt;&#x2009;68,000 laboratory results for &gt;&#x2009;12,500 patients and the algorithm generated score is being evaluated to review patients who are under consideration for transfer to ICU. No clinical decisions are being made based on output from the system. The HAVEN score has been computed using a PFA model for all these patients. The intent is that this score will be displayed on a graphical user interface for clinician review and response. CONCLUSIONS: The system uses a configurable PFA model to compute the HAVEN score which makes the system easily upgradable in terms of enhancing systems' predictive capability. Further system enhancements are planned to handle new data sources and additional management screens.</p

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

The Association between Supraphysiologic Arterial Oxygen Levels and Mortality in Critically Ill Patients. A Multicenter Observational Cohort Study.

Rationale: There is conflicting evidence on harm related to exposure to supraphysiologic PaO2 (hyperoxemia) in critically ill patients.Objectives: To examine the association between longitudinal exposure to hyperoxemia and mortality in patients admitted to ICUs in five United Kingdom university hospitals.Methods: A retrospective cohort of ICU admissions between January 31, 2014, and December 31, 2018, from the National Institute of Health Research Critical Care Health Informatics Collaborative was studied. Multivariable logistic regression modeled death in ICU by exposure to hyperoxemia.Measurements and Main Results: Subsets with oxygen exposure windows of 0 to 1, 0 to 3, 0 to 5, and 0 to 7 days were evaluated, capturing 19,515, 10,525, 6,360, and 4,296 patients, respectively. Hyperoxemia dose was defined as the area between the PaO2 time curve and a boundary of 13.3 kPa (100 mm Hg) divided by the hours of potential exposure (24, 72, 120, or 168 h). An association was found between exposure to hyperoxemia and ICU mortality for exposure windows of 0 to 1 days (odds ratio [OR], 1.15; 95% compatibility interval [CI], 0.95-1.38; P = 0.15), 0 to 3 days (OR 1.35; 95% CI, 1.04-1.74; P = 0.02), 0 to 5 days (OR, 1.5; 95% CI, 1.07-2.13; P = 0.02), and 0 to 7 days (OR, 1.74; 95% CI, 1.11-2.72; P = 0.02). However, a dose-response relationship was not observed. There was no evidence to support a differential effect between hyperoxemia and either a respiratory diagnosis or mechanical ventilation.Conclusions: An association between hyperoxemia and mortality was observed in our large, unselected multicenter cohort. The absence of a dose-response relationship weakens causal interpretation. Further experimental research is warranted to elucidate this important question