Feasibility of waveform capnography as a non-invasive monitoring tool during cardiopulmonary resuscitation

178 p.Sudden cardiac arrest (SCA) is one of the leading causes of death in the industrialized world and it includes the sudden cessation of circulation and consciousness, confirmed by the absence of pulse and breathing. Cardiopulmonary resuscitation (CPR) is one of the key interventions for patient survival after SCA, a life-saving procedure that combines chest compressions and ventilations to maintain a minimal oxygenated blood flow.To deliver oxygen, an adequate blood flow must be generated, by effective CPR, during the majority of the cardiac arrest time. Although monitoring the quality of CPR performed by rescuers during cardiac arrest has been a huge step forward in resuscitation science, in 2013, a consensus statement from the American Heart Association prioritized a new type of CPR quality monitoring focused on the physiological response of the patient instead of how the rescuer is doing.To that end, current resuscitation guidelines emphasize the use of waveform capnography during CPR for patient monitoring. Among several advantages such as ensure correct tube placement, one of its most important roles is to monitor ventilation rate, helping to avoid potentially harmful over-ventilation. In addition, waveform capnography would enable monitoring CPR quality, early detection of ROSC and determining patient prognosis. However, several studies have reported the appearance of fast oscillations superimposed on the capnogram, hereinafter CC-artifact, which may hinder a feasible use of waveform capnography during CPR. In addition to the possible lack of reliability, several factors need to be taken into account when interpreting ETCO2 measurements. Chest compressions and ventilation have opposing effects on ETCO2 levels. Chest compressions increase CO2 concentration, delivering CO2 from the tissues to the lungs, whilst ventilations remove CO2 from the lungs, decreasing ETCO2. Thus, ventilation rate acts as a significant confounding factor.This thesis analyzes the feasibility of waveform capnography as non-invasive monitoring tool of the physiological response of the patient to resuscitation efforts. A set of four intermediate goals was defined.First, we analyzed the incidence and morphology of the CC-artifact and assessed its negative influence in the detection of ventilations and in ventilation rate and ETCO2 measurement. Second, several artifact suppression techniques were used to improve ventilation detection and to enhance capnography waveform. Third, we applied a novel strategy to model the impact of ventilations and ventilation rate on the exhaled CO2 measured in out-of-hospital cardiac arrest capnograms, which could allow to measure the change in ETCO2 attributable to chest compressions by removing the influence of concurrent ventilations. Finally, we studied if the assessment of the ETCO2 trends during chest compressions pauses could allow to detect return of spontaneous circulation, a metric that could be useful as an adjunct to other decision tool

A pervasive body sensor network for monitoring post-operative recovery

Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces

Amplitude-integrated EEG assists in detecting cerebral dysfunction in the newborn

Background: Amplitude-integrated encephalography (aEEG) in term-born encephalopathic infants has been shown to be predictive of later neurodevelopmental outcomes, but little is known about the mediating cerebral pathology. In addition, the aEEG is commonly used to monitor electrographic seizures in the newborn, an important manifestation of cerebral pathology, but there is limited data on it’s efficacy for this purpose. It’s clinical application in the preterm infant remains to be explored. Aim: The central aim of this thesis is to prove the hypothesis that the aEEG assists in detecting cerebral dysfunction in the newborn. Methods: 1) In a cohort of term-born infants with encephalopathy and/or seizures digital aEEG background measures of the lower and upper aEEG margins were related to a numeric MRI abnormality score. 2) In at-risk term newborns, the accuracy of two-channel digital aEEG monitoring was compared with continuous concurrent conventional EEG for seizure detection. 3) In preterm infants (gestation at birth < 30 weeks) aEEG measures of lower and upper margin collected in the first week of life were compared in infants with substantial cerebral abnormality to infants without. Results: 1) For all infants in the term cohort, the severity of abnormality of aEEG background was strongly related to severity of abnormality seen on cerebral MRI. 2) Using the aEEG pattern with the raw EEG signal, 76% of electrographic seizures were correctly identified in the term infants. 3) In the preterm cohort, the lower and upper aEEG amplitude margins increased significantly during the first week of life. In the presence of substantial cerebral abnormality, these margins were significantly depressed. Seizures were noted in the smaller and sicker, infants. Conclusion: The central hypothesis of this thesis, that the aEEG assists in detecting cerebral dysfunction in the newborn was proved

Feasibility of waveform capnography as a non-invasive monitoring tool during cardiopulmonary resuscitation

178 p.Sudden cardiac arrest (SCA) is one of the leading causes of death in the industrialized world and it includes the sudden cessation of circulation and consciousness, confirmed by the absence of pulse and breathing. Cardiopulmonary resuscitation (CPR) is one of the key interventions for patient survival after SCA, a life-saving procedure that combines chest compressions and ventilations to maintain a minimal oxygenated blood flow.To deliver oxygen, an adequate blood flow must be generated, by effective CPR, during the majority of the cardiac arrest time. Although monitoring the quality of CPR performed by rescuers during cardiac arrest has been a huge step forward in resuscitation science, in 2013, a consensus statement from the American Heart Association prioritized a new type of CPR quality monitoring focused on the physiological response of the patient instead of how the rescuer is doing.To that end, current resuscitation guidelines emphasize the use of waveform capnography during CPR for patient monitoring. Among several advantages such as ensure correct tube placement, one of its most important roles is to monitor ventilation rate, helping to avoid potentially harmful over-ventilation. In addition, waveform capnography would enable monitoring CPR quality, early detection of ROSC and determining patient prognosis. However, several studies have reported the appearance of fast oscillations superimposed on the capnogram, hereinafter CC-artifact, which may hinder a feasible use of waveform capnography during CPR. In addition to the possible lack of reliability, several factors need to be taken into account when interpreting ETCO2 measurements. Chest compressions and ventilation have opposing effects on ETCO2 levels. Chest compressions increase CO2 concentration, delivering CO2 from the tissues to the lungs, whilst ventilations remove CO2 from the lungs, decreasing ETCO2. Thus, ventilation rate acts as a significant confounding factor.This thesis analyzes the feasibility of waveform capnography as non-invasive monitoring tool of the physiological response of the patient to resuscitation efforts. A set of four intermediate goals was defined.First, we analyzed the incidence and morphology of the CC-artifact and assessed its negative influence in the detection of ventilations and in ventilation rate and ETCO2 measurement. Second, several artifact suppression techniques were used to improve ventilation detection and to enhance capnography waveform. Third, we applied a novel strategy to model the impact of ventilations and ventilation rate on the exhaled CO2 measured in out-of-hospital cardiac arrest capnograms, which could allow to measure the change in ETCO2 attributable to chest compressions by removing the influence of concurrent ventilations. Finally, we studied if the assessment of the ETCO2 trends during chest compressions pauses could allow to detect return of spontaneous circulation, a metric that could be useful as an adjunct to other decision tool

Holistic System Design for Distributed National eHealth Services

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The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Deep Learning Algorithms for Time Series Analysis of Cardiovascular Monitoring Systems

This thesis investigates and develops methods to enable ubiquitous monitoring of the most examined cardiovascular signs, blood pressure, and heart rate. Their continuous measurement can help improve health outcomes, such as the detection of hypertension, heart attack, or stroke, which are the leading causes of death and disability. Recent research into wearable blood pressure monitors sought predominately to utilise a hypothesised relationship with pulse transit time, relying on quasiperiodic pulse event extractions from photoplethysmography local signal characteristics and often used only a fraction of typically bivariate time series. This limitation has been addressed in this thesis by developing methods to acquire and utilise fused multivariate time series without the need for manual feature engineering by leveraging recent advances in data science and deep learning methods that showed great data analysis potential in other domains