Louis Atallah

Philips | Philips · Philips Healthcare

Objectives: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. Design: Retrospective, multicenter study of ICU patie...

Introduction: SARS-CoV-2 (COVID-19) has increased the burden on ICUs leading to a rise in patient admissions, severity of illness, length of stay (LOS), and mortality. Objectives: The objectives are to investigate the effects of COVID-19 on patient demographics, clinical outcomes, and treatments for a large dataset spanning 11 US health systems wi...

Clinicians strive to maintain normothermia, which requires measurement of core-body temperature and may necessitate active warming of patients. Monitoring temperature currently requires invasive probes. This work investigates a novel foam-based flexible sensor worn behind the ear for the measurement of core body temperature. This observational stud...

Background Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care...

Aim To address alarm fatigue, a new alarm management system which ensures a quicker delivery of alarms together with waveform information on nurses’ handheld devices was implemented and settings optimized. The effects of this clinical implementation on alarm rates and nurses’ responsiveness were measured in an 18‐bed single‐family rooms neonatal in...

Background: Patient monitoring devices are responsible for producing many false alarms, leading to desensitization and alarm fatigue. More intelligent alarm management can lead to an improved clinical workflow with positive effects on patient safety. Until recently, the NICU of Máxima Medical Center (MMC), Veldhoven, used a distributed alarm manage...

Introduction Early warning scores (EWS) are being increasingly embedded in hospitals over the world due to their promise to reduce adverse events and improve the outcomes of clinical patients. The aim of this study was to evaluate the clinical use of an automated modified EWS (MEWS) for patients after surgery. Methods This study conducted retrospe...

Most deaths occurring due to a surgical intervention happen postoperatively rather than during surgery. The current standard of care in many hospitals cannot fully cope with detecting and addressing post-surgical deterioration in time. For millions of patients, this deterioration is left unnoticed, leading to increased mortality and morbidity. Post...

Sleep is important for the development of preterm infants. During sleep, neural connections are formed and the development of brain regions is triggered. In general, various rudimentary sleep states can be identified in the preterm infant, namely active sleep (AS), quiet sleep (QS) and intermediate sleep (IS). As the infant develops, sleep states c...

Objective: To determine whether heart rate variability (HRV) can serve as a surrogate measure to track regulatory changes during kangaroo care, a period of parental coregulation distinct from regulation within the incubator. Study design: Nurses annotated the starting and ending times of kangaroo care for 3 months. The pre-kangaroo care, during-...

Investigation of heart rate variability features for separating sleep states for premature infant

Patient monitoring generates a large number of alarms, the vast majority of which are false. Excessive non-actionable medical alarms lead to alarm fatigue, a well-recognized patient safety issue. While multiple approaches to reduce alarm fatigue have been explored, patterns in alarming and inter-alarm relationships, as they manifest in the clinical...

Compared to cabled ECG devices, the use of wearable patches to reconstruct ECG offers a more comfortable alternative for continuous monitoring, especially for patients at home. In this work, we investigate the feasibility of synthesizing a 3-lead ECG signal from 3 separate wearable and wireless patches. We also investigate the effect of their orien...

Total knee replacement currently lacks robust indications and objective follow-up metrics. Patients and healthcare staff are under-equipped to optimise outcomes. This study aims to investigate the feasibility of using an ear-worn motion sensor (e-AR, Imperial College London) to conduct objective, home-based mobility assessments in the peri-operativ...

The temperature of preterm neonates must be maintained within a narrow window to ensure their survival. Continuously measuring their core temperature provides an optimal means of monitoring their thermoregulation and their response to environmental changes. However, existing methods of measuring core temperature can be very obtrusive, such as recta...

Introduction: Continuous monitoring of core temperature in neonates is important for understanding the baby's thermal state and maintaining an optimal thermal environment. However, familiar methods for measuring core temperature continuously, such as rectal temperature probes, can be very obtrusive. This study compares the temperature performance o...

Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accel...

Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder related gait abnormalities. Traditional marker-based systems are well known for tracking gait parameters for gait analysis, however, it requires l...

The thin skin of preterm babies is easily damaged by adhesive electrodes, tapes, chest drains and needle-marks. The scars caused could be disfiguring or disabling to 10% of preterm newborns. Capacitive sensors present an attractive option for pervasively monitoring neonatal ECG, and can be embedded in a support system or even a garment worn by the...

Measuring gait asymmetry is an important feature when characterizing functional imbalance between limbs. This could be due to pathologies, such as osteoarthritis, stroke, or associated with the effects of surgeries such as hip arthroplasty. Generally, the study of asymmetry or imbalance has required the use of a gait lab or force plates, which coul...

Accurate estimation of daily total Energy Expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance and estimation methods used. This paper examines whether a single ear-worn accele...

Surgery to the trunk often results in a change of gait, most pronounced during walking. This change is usually transient, often as a result of wound pain, and returns to normal as the patient recovers. Quantifying and monitoring gait impairment therefore represents a novel means of functional postoperative home recovery follow-up. Until now, this t...

This paper proposes the use of singular spectrum analysis (SSA) to segment and classify human activities in real time by using an ear-worn Activity Recognition (e-AR) sensor. A similarity measure is calculated using SSA to construct a 3D feature vector from the 3 axes of e-AR signal. An algorithm based on the concept of clustering and buffering is...

The prevalence of obesity worldwide presents a great challenge to existing healthcare systems. There is a general need for pervasive monitoring of the dietary behaviour of those who are at risk of co-morbidities. Currently, however, there is no accurate method of assessing the nutritional intake of people in their home environment. Traditional meth...

Important challenges facing global healthcare include the increase in the number of people affected by escalating healthcare costs, chronic and infectious diseases, the need for better and more affordable elderly care and expanding urbanisation combined with air and water pollution. Recent advances in pervasive sensing technologies have led to mini...

Skeletal muscle weakness is an important complication of chronic respiratory disease. The effect of acute exacerbations on strength in patients with cystic fibrosis is not known. Quadriceps (QMVC) and respiratory muscle strength were measured in patients at the time of acute admission, at discharge and one month later. Patients wore an activity mon...

Wireless sensor networks enable continuous and reliable data acquisition for real-time monitoring in a variety of application areas. Due to the large amount of data collected and the potential complexity of emergent patterns, scalable and distributed reasoning is preferable when compared to centralised inference as this allows network wide decision...

A force-plate instrumented treadmill (Hp Cosmos Gaitway) was used to validate the use of a miniaturised lightweight ear-worn sensor (7.4 g) for gait monitoring. Thirty-four healthy subjects were asked to progress up to their maximum walking speed on the treadmill (starting at 5 km/h, with 0.5 km increments). The sensor houses a 3D accelerometer whi...

Activities of daily living are important for assessing changes in physical and behavioral profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers have been used widely in wearable devices for activity classification, the positioning of the sensors and the selection of re...

Activity monitoring is important for assessing daily living conditions for elderly patients and those with chronic diseases. Transitions between activities can present characteristic patterns that may be indicative of quality of movement. To detect and analyze transitional activities, a manifold-based approach is proposed in this paper. The propose...

Falling is one of the leading causes of serious health decline or injury-related deaths in the elderly. For survivors of a fall, the resulting health expenses can be a devastating burden, largely because of the long recovery time and potential comorbidities that ensue. The detection of a fall is, therefore, important in care of the elderly for decr...

A progressive improvement in gait following knee arthroplasty surgery can be observed during walking and transitional activities such as sitting/standing. Accurate assessment of such changes traditionally requires the use of a gait lab, which is often impractical, expensive, and labour intensive. Quantifying gait impairment following knee arthropla...

Patients' functional recovery at home following surgery may be evaluated by monitoring their activities of daily living. Existing tools for assessing these activities are labor-intensive to administer and rely heavily on recall. This study describes the use of a wireless ear-worn activity recognition sensor to monitor postoperative activity levels...

This study aimed to predict human energy expenditure and activity type using a miniature lightweight ear-worn inertia sensor and a novel pattern recognition algorithm for activity detection. This study used a protocol of 11 activities of daily living: lying down, standing, computer work, vacuuming, stairs, slow walking, brisk walking, slow running,...

Due to the natural aging process, the risks associated with falling can increase significantly. For the elderly, this usually marks a rapid deterioration of their health. While there are identified strategies that can be adopted to reduce the number of falls, it is still not possible to prevent all falls. Clinically, the Tinetti Gait and Balance As...

The FP6 project “Wireless Accessible Sensor Populations” (WASP) has developed an end-to-end infrastructure for the deployment and enterprise integration of wireless sensor nodes. The infrastructure is generic and allows for optimisation for a variety of applications by the development of dedicated services that can be distributed over (wearable and...

Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of...

New approaches to chronic disease management within a home or community setting offer patients the prospect of more individually focused care and improved quality of life. This paper investigates the use of a light-weight ear worn activity recognition device combined with wireless ambient sensors for identifying common activities of daily living. A...

With the maturity of sensing and pervasive computing techniques, extensive research is being carried out in using different sensing techniques for understanding human behaviour. An introduction to key modalities of pervasive sensing is presented. Behaviour modelling is then highlighted with a focus on probabilistic models. The survey discusses disc...

Laparoscopic surgery is a challenging task in minimally invasive surgery, which involves complex instrument control, extensive manual dexterity, and hand-eye coordination. This requires a greater attention to training and skills evaluation. In order to provide a more objective skills assessment method, this paper presents a wireless sensor platform...

This paper investigates an ear worn sensor for the development of a gait analysis framework. Instead of explicitly defining gait features that indicate injury or impairment, an automatic method of feature extraction and selection is proposed. The proposed framework uses multi-resolution wavelet analysis and margin based feature selection. It was va...

Activity monitoring is an important part of pervasive sensing, particularly for assessing activities of daily living for elderly patients and those with chronic diseases. Previous studies have mainly focused on binary transitions between activities, but have overlooked detailed transitional patterns. For patient studies, this transition period can...

Pervasive healthcare provides an effective solution for monitoring the wellbeing of elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson's. However, developing functional pervasive systems is a complex task that entails the creation of appropriate sensing platforms, inte...

The prefrontal cortex (PFC) is known to be vital for acquisition of visuomotor skills, but its role in the attainment of complex technical skills which comprise both perceptual and motor components, such as those associated with surgery, remains poorly understood. We hypothesized that the prefrontal response to a surgical knot-tying task would be h...

Over the past decade, miniaturization and cost reduction in semiconductors have led to computers smaller in size than a pinhead with powerful processing abilities that are affordable enough to be disposable. Similar advances in wireless communication, sensor design and energy storage have meant that the concept of a truly pervasive 'wireless sensor...

Effective hand-eye coordination is an important aspect of training in laparoscopic surgery. This paper investigates the interdependency of the hand and eye movement along with the variability of their temporal relationships based on Granger-causality. Partial directed coherence is used to reveal the subtle effects of improvement in hand-eye coordin...

This paper presents a framework based on Gaussian Processes for assessing cross channel consensus in Body Sensor Network (BSN) data. Cross channel consensus can be observed by measuring the prediction error of one channel given the others, which could help in predicting missing data, correcting for noisy channels, or learning relationships between...

Pervasive sensing is set to transform the future of patient care by continuous and intelligent monitoring of patient well-being. In practice, the detection of patient activity patterns over different time resolutions can be a complicated procedure, entailing the utilisation of multi-tier software architectures and processing of large volumes of dat...

To investigate neurocognitive mechanisms associated with task-related expertise development, this paper investigates serial changes in prefrontal activation patterns using functional near infrared spectroscopy (fNIRS). We evaluate cortical function in 62 healthy subjects with varying experience during serial evaluations of a knot-tying task. All ta...

This paper presents an application of a service-based architecture to pervasive monitoring of the elderly using ambient and wearable sensors. The design consideration of the model addresses heterogeneous computing and network resource utilization, allowing inter-operability and supporting dynamic environments to achieve system wide resource optimiz...

Laparoscopic surgery poses many different constraints for the operating surgeon, resulting in a slow uptake of advanced laparoscopic procedures. Traditional approaches to the assessment of surgical performance rely on prior classification of a cohort of surgeons' technical skills for validation, which may introduce subjective bias to the outcome. I...

Patients going home following major surgery are susceptible to complications such as wound infection, abscess formation, malnutrition, poor analgesia, and depression, all of which can develop after the fifth postoperative day and slow recovery. Although current hospital recovery monitoring systems are effective during perioperative and early postop...

Laparoscopic surgical training is a challenging task due to the complexity of instrument control and demand on manual dexterity and hand-eye coordination. Currently, training and assessing surgeons for their laparoscopic skills rely mainly on subjective assessment. This paper presents a Body Sensor Network (BSN) sensor glove for laparoscopic gestur...

Monitoring expertise development in surgery is likely to benefit from evaluations of cortical brain function. Brain behaviour is dynamic and nonlinear. The aim of this paper is to evaluate the application of a nonlinear dimensionality reduction technique to enhance visualisation of multidimensional functional Near Infrared Spectroscopy (fNIRS) data...

Post surgical care is an important part of the surgical recovery process. With the introduction of minimally invasive surgery (MIS), the recovery time of patients has been shortened significantly. This has led to a shift of postoperative care from hospital to home environment. To prevent the occurrence of adverse events, the care of these patients...

This paper investigates the combined use of ambient and wearable sensing for inferring changes in patient behaviour patterns. It has been demonstrated that with the use of wearable and blob based ambient sensors, it is possible to develop an effective visualization framework allowing the observation of daily activities in a homecare environment. An...

Despite technological advances in minimally invasive surgery (MIS) in recent years, D visualization of the operative field still remains one of greatest challenges. In this paper, the effect of three visualization techniques including conventional 2D, 2D with enhanced depth cue based on shadow, and active 3D displays for novices with no prior adapt...

Side-scan sonar is now considered 'the instrument-of-choice' for underwater archaeological surveys. However, much work is required to understand the factors that may affect the surveyed data, including the effect of sonar resolution on the detection of objects on the seafloor and the potential confusion between the presence of recent objects on the...

This work sheds the light on an important problem that faces real-world texture classification. That of incorporating textural information present at several scales and the robustness of classifiers to viewing distance and zooming. A Markov Random field framework is considered and the Varma-Zisserman classifier [16] (VZ classifier) is used as a sta...

The authors present a technique for making use of both sidescan amplitude and bathymetric data provided from sidescan bathymetric sonars for the classification of underwater seabeds. Sidescan amplitude is corrected for physical factors and used to plot 'processed' sidescan images. Both amplitude and textural features are derived from these images....

This work is concerned with the automatic characterisation and classification of seabed sediments by using wavelet transform techniques to analyse the incoming one-dimensional signals from both sidescan and sidescan bathymetric sonars. This method extracts features from the energies at different scales of the wavelet transform of the signal then us...

In this paper we examine the use of bathymetric sidescan sonar for automatic classification of seabed sediments. Bathymetric sidescan sonar, here implemented through a small receiver array, retains the advantage of sidescan in speed through illuminating large swaths, but also enables the data gathered to be located spatially. The spatial location a...

In several sonar studies, bathymetric information; is used for the correction of amplitude data and the calculation of backscattering strength, which is plotted versus grazing angle and used for seabed classification. Bathymetric data is also used as an easily viewed backdrop to visualize backscattered sonar data in surveys. This work proposes an a...

The European project "Wireless Accessible Sensor Populations" (WASP) aims to develop cost-efficient solutions to facilitate WSN deployment and application-driven optimization. This presentation addresses the deployment set-up and trials undertaken to enable continuous monitoring of elderly persons using unobtrusive (body-worn and ambient) wireless...