Marco Pimentel

University of Oxford | OX · Department of Engineering Science

The challenges presented by the Coronavirus disease 2019 (COVID-19) pandemic to the National Health Service (NHS) in the United Kingdom (UK) led to a rapid adaptation of infection disease protocols in-hospital. In this paper we report on the optimisation of our wearable ambulatory monitoring system (AMS) to monitor COVID-19 patients on isolation wa...

Background: Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms; however, their reliability in usual hospital settings is not known. Objective: We report...

BACKGROUND Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms, however, their reliability in usual hospital settings is not known. OBJECTIVE We report the...

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The...

Background: The global health pandemic of coronavirus disease 2019 (COVID-19) is placing a huge strain on UK hospitals. Early studies suggest that patients can deteriorate quickly after admission to hospital. The aim of this study was to model changes in vital signs for patients hospitalised with COVID-19. Methods: This was a retrospective obser...

Rationale. Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with early warning score (EWS) systems being used to identify those at risk of deterioration. Objectives. We report the development and...

Objectives National guidelines for identifying physiological deterioration and sepsis in hospitals depend on thresholds for blood pressure that do not account for age or sex. In populations outside hospital, differences in blood pressure are known to occur with both variables. Whether these differences remain in the hospitalised population is unkno...

Introduction Automated continuous ambulatory monitoring may provide an alternative to intermittent manual vital signs monitoring. This has the potential to improve frequency of measurements, timely escalation of care and patient safety. However, a major barrier to the implementation of these wearable devices in the ward environment is their uncerta...

INTRODUCTION. Timely recognition and escalation of physiological indicators of worsening conditions in acute hospital wards remains challenging. Vital signs monitoring using Early Warning Scoring (EWS) is time consuming and the prescribed observation frequency may be suboptimal or undeliverable. Wearable ambulatory monitoring systems (AMS) may prov...

Objectives: To calculate fractional inspired oxygen concentration (FiO2) thresholds in ward patients and add these to the National Early Warning Score (NEWS). To evaluate the performance of NEWS-FiO2 against NEWS when predicting in-hospital death and unplanned intensive care unit (ICU) admission. Methods: A multi-centre, retrospective, observati...

Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors on wards to monitoring patients in their own homes. The problem is still very challenging, particularly during motion for large segments of data, where results from different methods often don't agr...

Aims: To compare the ability of the National Early Warning Score (NEWS) and the National Early Warning Score 2 (NEWS2) to identify patients at risk of in-hospital mortality and other adverse outcomes. Methods: We undertook a multi-centre retrospective observational study at five acute hospitals from two UK NHS Trusts. Data were obtained from com...

Aim: The National Early Warning System (NEWS) is based on vital signs; the Laboratory Decision Tree Early Warning Score (LDT-EWS) on laboratory test results. We aimed to develop and validate a new EWS (the LDTEWS:NEWS risk index) by combining the two and evaluating the discrimination of the primary outcome of unanticipated intensive care unit (ICU...

Background: Data smoothing of vital signs has been reported in the anesthesia literature, suggesting that clinical staff are biased toward measurements of normal physiology. However, these findings may be partially explained by clinicians interpolating spurious values from noisy signals and by the undersampling of physiological changes by infreque...

With the increase in volume of wearable sensors, there exists the possibility of personalising patient care, employing automated algorithms. However, automated algorithms are typically less reliable than gold-standard expert labels; the latter are scarce and expensive. In real-life applications, expert labels are not available, and algorithms for p...

Aims of study: To develop and validate a centile-based early warning score using manually-recorded data (mCEWS). To compare mCEWS performance with a centile-based early warning score derived from continuously-acquired data (from bedside monitors, cCEWS), and with other published early warning scores. Materials and methods: We used an unsupervise...

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 p...

Objective: Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are highly dependent on patient physiology, regardless of general signal quality. The present work describes an RR estimation algorithm using respi...

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 a...

Purpose: Serum sodium derangement is the most common electrolyte disturbance among patients admitted to intensive care. This study aims to validate the association between dysnatremia and serum sodium fluctuation with mortality in surgical intensive care patients. Method: We performed a retrospective analysis of the Medical Information Mart for...

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 clin...

Algorithms for identification of deteriorating patients from electronic health records (EHRs) fuse vital sign data, which can be measured at the bedside, with additional physiological data from the EHR. It has been observed that these algorithms provide improved performance over traditional early warning scores (EWSs), which are restricted to the u...

Respiratory rate (RR), a key vital sign for measuring one's status of health, can be estimated and monitored using affordable wearable devices such as a pulse oximeter. Automated algorithms can be used to derive RR from the photoplethysmogram (PPG) which is measured by the pulse oximeter, but they are less reliant due to their large inter-and intra...

The step-down unit (SDU) is a high-acuity hospital environment, to which patients may be sent after discharge from the intensive care unit (ICU). About 1- in-7 patients will deteriorate in the SDU and require emergency readmission to the ICU. Upon readmission, these patients experience significantly higher mortality risks and lengths of stay. Gauss...

Respiratory rate (RR) is one of the most informative indicators of a patient's health status. However, automated, non-invasive measurements of RR are insufficiently robust for use in clinical practice. A number of methods have been described in the literature to estimate RR from both photo-plethysmography (PPG) and electrocardiography (ECG) based o...

Previous work has been demonstrated that tracking features describing the dynamic and time-varying patterns in brain monitoring signals provide additional predictive information beyond that derived from static features based on snapshot measurements. To achieve more accurate predictions of outcomes of patients with traumatic brain injury (TBI), we...

This is a sample Chapter (4) published in Telemedicine and Electronic Medicine Handbook, Vol. 1, 2016, ISBN: 13:978-1-4822-3658-3, edited by Halit Eren and John G Webster. Referencing should directly be made to chapter authors.

Limited information exists on the etiology, prevalence, and significance of hyperdynamic left ventricular ejection fraction (HDLVEF) in the intensive care unit (ICU). Our aim in the present study was to compare characteristics and outcomes of patients with HDLVEF with those of patients with normal left ventricular ejection fraction in the ICU using...

Accurate heart beat detection in signals acquired from intensive care unit (ICU) patients is necessary for establishing both normality and detecting abnormal events. Detection is normally performed by analysing the electrocardiogram (ECG) signal, and alarms are triggered when parameters derived from this signal exceed preset or variable thresholds....

Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive...

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical not...

IntroductionThe neutrophil-to-lymphocyte ratio (NLR) is a biological marker that has been shown to be associated with outcomes in patients with a number of different malignancies. The objective of this study was to assess the relationship between NLR and mortality in a population of adult critically ill patients.Methods We performed an observationa...

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical not...

Respiration rate (RR) is a physiological parameter that is typically used in clinical settings for monitoring patient condition. Consequently, it is measured in a wide range of clinical scenarios, notably absent from which is measurement using wearable sensors. With increasing numbers of patients being monitored via wearable sensors, as described b...

In extracranial robotic radiotherapy, tumour motion due to respiration is compensated based external markers. Two models are typically used to enable a real-time adaptation. A prediction model, which compensates time latencies of the treatment systems due to e.g. kinematic limitations, and a correlation model, which estimates the internal tumour po...

Gaussian process (GP) models are a flexible means of performing non-parametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time-series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariat...

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines-including clinical medicine, computer sci...

The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeter's photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors,...

The intensive care unit (ICU) admits the most severely ill patients, and the goal of the unit can be interpreted as stabilizing patient physiology. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff. Early detection of physiological deterioration has been highlight...

Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as “one-class classification”, in which a model is constructed to describe “normal” training data. The novelty detection approach is typically used when the quantity of available “abnormal” data is in...

Gaussian process (GP) models are a flexible means of performing non-parametric Bayesian regression. However, the majority of existing work using GP models in healthcare data is defined for univariate output time-series, denoted as single-task GPs (STGP). Here, we investigate how GPs could be used to model multiple correlated univariate physiologica...

The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allow...

The automatic detection of heartbeats within physiological signals collected from patients connected to bedside monitors is an important task as it allows the detection of pathological conditions. Heartbeat detection is traditionally performed using the ECG. However, all bedside monitors are prone to missing data, yet it is rare for any system to i...

The burden of hypertension-related illness is greatest in low-resource settings. Barriers to its treatment include limited access to accurate Blood Pressure (BP) monitors, data on variation of BP readings over time, and training on how and when to take reliable BP readings. We developed an affordable smartphone-based BP monitor that records the pre...

Recognition of complex trajectories in multivariate time-series data requires effective models and representations for the analysis and matching of functional data. In this work, we introduce a new representation that allows for matching of noisy, and unevenly-sampled trajectories, and we explore whether this representation may be used to character...

The presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function to model the frequency- and amplitude-modulation ti...

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 use...

Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into "intelligent" analysis methods that are sufficiently robust to support large-scale depl...

The new clinically available arterial spin labeling (ASL) perfusion imaging sequences present some advantages relatively to the commonly used blood oxygen level-dependent (BOLD) method for functional brain studies using magnetic resonance imaging (MRI). In particular, regional cerebral blood flow (CBF) changes are thought to be more directly relate...

The current standard of clinical practice for patient monitoring in most developed nations is connection of patients to vital-sign monitors, combined with frequent manual observation. In some nations, such as the UK, manual early warning score (EWS) systems have been mandated for use, in which scores are assigned to the manual observations, and car...

Deterioration in Patients who undergo upper-gastrointestinal surgery may be evident in the vital signs prior to adverse events. A dataset comprising observational vital-sign data from 128 post-operative patients was used to explore the trajectory of patients vital-sign changes during their stay in the post-operative ward. A model of normality based...

Taking the Blood Pressure (BP) with a traditional sphygmomanometer requires a trained user. In developed countries, patients who need to monitor their BP at home usually acquire an electronic BP device with an automatic inflate/deflate cycle that determines the BP through the oscillometric method. For patients in resource constrained regions automa...

Arterial spin labeling (ASL) is a non-invasive MRI technique that allows the quantitative measurement of perfusion, (regional cerebral blood flow (rCBF)). The ASL techniques use the labeling of the blood, by inverting or saturating the spins of water molecules of the blood supplying the imaged region. When reaching the capillary bed, these will be...

Figure 1: Localization of the HMC: 9 points over the segment of the precentral gyrus (in red) and two calculated mean points (in blue), defined over 4 axial slices in the MNI standard space. Figure 3: Inter-subject variability of BOLD (red), BOLD ASL (green) and ASL (blue) activation clusters: cluster COGs and local maxima are shown for all subject...