Yes / Avoidable hospital mortality is often attributable to inadequate patient vital signs monitoring, and failure to recognise or respond to clinical deterioration. The processes involved with vital sign collection and charting; their integration, interpretation and analysis; and the delivery of decision support regarding subsequent clinical care are subject to potential error and/or failure. Objective To determine whether introducing an electronic physiological surveillance system (EPSS), specifically designed to improve the collection and clinical use of vital signs data, reduced hospital mortality. Methods A pragmatic, retrospective, observational study of seasonally adjusted in-hospital mortality rates in three main hospital specialties was undertaken before, during and after the sequential deployment and ongoing use of a hospital-wide EPSS in two large unconnected acute general hospitals in England. The EPSS, which uses wireless handheld computing devices, replaced a paper-based vital sign charting and clinical escalation system. Results During EPSS implementation, crude mortality fell from a baseline of 7.75% (2168/27 959) to 6.42% (1904/29 676) in one hospital (estimated 397 fewer deaths), and from 7.57% (1648/21 771) to 6.15% (1614/26 241) at the second (estimated 372 fewer deaths). At both hospitals, multiyear statistical process control analyses revealed abrupt and sustained mortality reductions, coincident with the deployment and increasing use of the system. The cumulative total of excess deaths reduced in all specialties with increasing use of the system across the hospital. Conclusions The use of technology specifically designed to improve the accuracy, reliability and availability of patients’ vital signs and early warning scores, and thereby the recognition of and response to patient deterioration, is associated with reduced mortality in this study.

The development of internet-based technologies, the design and adoption of wireless wearable and smart devices have been a growing study spot in all domains. The healthcare sector as many others is making technological progress to improve healthcare services and patients wellbeing and avoid or minimize the use of manual and traditional practices such as the use of paper notes to record the vital signs parameters data. The vital signs parameters are the most monitored physiology features, they produce a big amount of data and request a close follow up to define the health condition of a patient. Continuous vital signs monitoring involves the usage of different devices and systems, which if appropriate positively impact the activities involved, by enabling the continuous generation of data and information about the overall health status of patients and contribute to the wellbeing of individuals, in terms of preventing and reducing fatal risks. To investigate this situation, this research’s focus was in three parts; first, investigate recent research about patient’s health predictions based on vital signs parameters and the impacts of continuous monitoring on the care given. Second, explore the availability in terms of i.e. sensors used in devices that can continuously track vital signs parameters. Last, to provide a possible design recommendation to improve and/or replace the existing devices for vital signs parameters measuring and monitoring in emergency and post-operative care. A qualitative approach and participatory design approach were used to collect data. The qualitative part was achieved through interviews and the participatory design part was accomplished by the future workshop and two prototyping techniques, paper and digital prototypes. The findings of this research were analysed using conceptual analysis, and also discussed using those concepts. Together with the participants, this research resulted in three design suggestions which if implemented shall improve the vital signs continuous monitoring activities, by facilitating the healthcare professionals in their clinical responsibilities and improving the patients wellbeing while admitted in Emergency and Post-operative wards.

abstract: In the past half century, low-power wireless signals from portable radar sensors, initially continuous-wave (CW) radars and more recently ultra-wideband (UWB) radar systems, have been successfully used to detect physiological movements of stationary human beings. The thesis starts with a careful review of existing signal processing techniques and state of the art methods possible for vital signs monitoring using UWB impulse systems. Then an in-depth analysis of various approaches is presented. Robust heart-rate monitoring methods are proposed based on a novel result: spectrally the fundamental heartbeat frequency is respiration-interference-limited while its higher-order harmonics are noise-limited. The higher-order statistics related to heartbeat can be a robust indication when the fundamental heartbeat is masked by the strong lower-order harmonics of respiration or when phase calibration is not accurate if phase-based method is used. Analytical spectral analysis is performed to validate that the higher-order harmonics of heartbeat is almost respiration-interference free. Extensive experiments have been conducted to justify an adaptive heart-rate monitoring algorithm. The scenarios of interest are, 1) single subject, 2) multiple subjects at different ranges, 3) multiple subjects at same range, and 4) through wall monitoring. A remote sensing radar system implemented using the proposed adaptive heart-rate estimation algorithm is compared to the competing remote sensing technology, a remote imaging photoplethysmography system, showing promising results. State of the art methods for vital signs monitoring are fundamentally related to process the phase variation due to vital signs motions. Their performance are determined by a phase calibration procedure. Existing methods fail to consider the time-varying nature of phase noise. There is no prior knowledge about which of the corrupted complex signals, in-phase component (I) and quadrature component (Q), need to be corrected. A precise phase calibration routine is proposed based on the respiration pattern. The I/Q samples from every breath are more likely to experience similar motion noise and therefore they should be corrected independently. High slow-time sampling rate is used to ensure phase calibration accuracy. Occasionally, a 180-degree phase shift error occurs after the initial calibration step and should be corrected as well. All phase trajectories in the I/Q plot are only allowed in certain angular spaces. This precise phase calibration routine is validated through computer simulations incorporating a time-varying phase noise model, controlled mechanic system, and human subject experiment. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

Abstract The main objective of this thesis project is to implement a wireless vital signs monitoring system for measuring the ECG of a patient in the home environment. The research focuses on two specific research objectives: 1) the development of a distributed healthcare system for vital signs monitoring using wireless sensor network devices and 2) a practical test and performance evaluation for the reliability for such low-rate wireless technology in ubiquitous health monitoring applications. The first section of the thesis describes the design and implementation of a ubiquitous healthcare system constructed from tiny components for the home healthcare of elderly persons. The system comprises a smart shirt with ECG electrodes and acceleration sensors, a wireless sensor network node, a base station and a server computer for the continuous monitoring of ECG signals. ECG data is a commonly used vital sign in clinical and trauma care. The ECG data is displayed on a graphical user interface (GUI) by transferring it to a PDA or a terminal PC. The smart shirt is a wearable T-shirt designed to collect ECG and acceleration signals from the human body in the course of daily life. In the second section, a performance evaluation of the reliability of IEEE 802.15.4 low-rate wireless ubiquitous health monitoring is presented. Three scenarios of performance studies are applied through practical tests: 1) the effects of the distance between sensor nodes and base-station, 2) the deployment of the number of sensor nodes in a network and 3) data transmission using different time intervals. These factors were measured to analyse the reliability of the developed technology in low-rate wireless ubiquitous health monitoring applications. The results showed how the relationship between the bit-error-rate (BER) and signal-to-noise ratio (SNR) was affected when varying the distance between sensor node and base-station, through the deployment of the number of sensor nodes in a network and through data transmission using different time intervals.

Total quality management (TQM) approach is often used to carry out company-wide continuous quality improvement plans in manufacturing and service industries. Similarly, TQM can also play a critical role for quality management in health care. Aiming to improve health care quality, experiences showed that major problems of non-patient care, patient records and vital signs monitoring are encountered. In this study, we aim to introduce TQM for quality improvement for intensive care unit (ICU) operations, including some solutions and the prototype of quality management. And vital signs monitoring at ICU is taken as an example of process. For quality improvement of non-patient care, Health Care Quality Development Life Cycle, including (1) quality requirement analysis, (2) quality specification review, (3) quality design, (4) quality implementation, (5) quality testing, (6) quality maintaining, and (7) quality validation, is discussed. The prototype of the first three phases for quality improvement at ICU is explored. Through quality requirement analysis, non-patient care quality at ICU is defined in areas of administration, facility and environment. For quality improvement of patient records maintaining, firstly, scope of health care information systems is categorized as administrative operational system, decision support system, clinical information system, and medical information system. According to this categorization and experience, some interesting result is found. For instance, the current applications of information systems for teaching hospitals in southern Taiwan surveyed are that most applications are administrative and clinical. And the essential information of patient records used in each information system is not complete or not easily accessed. Model of the patient record maintaining is introduced and the prototype design of patient records is recommended for quality improvement of patient records maintaining at ICU. To improve quality of vital signs monitoring is one essential requirement and specification for ICU quality improvement. Effective outcome measures of vital signs monitoring and early detecting of abnormal vital signs is considered important. For quality improvement of vital signs monitoring at ICU, heart rate graphs are taken as examples in our study through the heart rate graphs monitoring. Health professionals can understand the interactions of human autonomic nervous system. By use of digitizer, the computable heart rate data is acquired from each graph and grouped into mortality and near-to-normal cases. Then spectrum form of heart rate data, describing more about heart function, is used for statistical analysis. Several control chart methods have been experimented to detect small heart rate shifts from target, cumulative sum control chart (Cusum) is adopted in our study. The observable variable is the patient¡¦s heart rate, the purpose is to check the alarms pointed out by Cusum that could be partially be ascribed to changes of heart rate trend over time, and to a shift in the monitoring process mean. From summaries of nonconformities in the Cusum charts, mortality cases obviously have more nonconformities. It is obvious that Cusum control charts of mortality cases provide diagnostic information for vital signs monitoring process. In addition, Cusum charts may also inform ICU professionals that there is a small shift of patient heart rate, a continuously increasing or decreasing heart rate, and the adjustment of sympathetic nerve and parasympathetic nerve. In those cases, some special care is needed.

The vital signs monitoring data of an infant receiving intensive care are a rich source of information about its health condition. One major concern about the state of health of such patients is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle and current diagnosis procedures involve slow laboratory testing, sepsis detection based on the monitored physiological dynamics is a clinically significant task. This challenging problem can be thoroughly modelled as real-time inference within a machine learning framework. In this thesis, we develop probabilistic dynamical models centred around the goal of providing useful predictions about the onset of neonatal sepsis. This research is characterised by the careful incorporation of domain knowledge for the purpose of extracting the infant’s true physiology from the monitoring data. We make two main contributions. The first one is the formulation of sepsis detection as learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detection of neonatal sepsis. In addition, the proposed approach involves exact marginalisation over missing data at inference time. When applying the ARHMM on a real-world dataset, we found that it can produce effective predictions about the onset of sepsis. Second, both sepsis and clinical event detection are formulated as learning and inference in a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical systems where complex interactions between modes of operation can be represented as a twolevel hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis. The model parameterisation and estimation procedures are adapted to the specifics of physiological monitoring data. We demonstrate that the performance of the HSLDS for the detection of sepsis is not statistically different from the AR-HMM, despite the fact that the latter model is given “ground truth” annotations of the patient’s physiology.

Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-tonoise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.