Wearable devices make a significant contribution in health monitoring systems by continuously collecting important health parameters with the intention of prevention, early detection and control of diseases. Photoplethysmography (PPG) appears as a feasible, affordable and non-invasive technique for long term health monitoring and is commonly used in various wearable systems and medical devices. However, PPG signal is highly prone to different noise sources such as motion and ambient light which affect the reliability of the estimated results of health metrics. Therefore, a measure of quality, signal quality index (SQI), is needed for identifying poor signals distorted by
This book provides a comprehensive overview of the state of the art in signal quality assessment techniques for physiological signals, and chiefly focuses on ECG (electrocardiography) and PPG (photoplethysmography) signals obtained from wearable sensors in ambulatory clinical settings. It presents the techniques currently proposed by leading researchers, as well as examples using data from clinical trials on wearable sensors for inpatient and outpatient settings. In addition, the book assesses current approaches through a practical lens by discussing the implications of deploying the various proposed systems for clinical practices and health outcomes. As such, it will be of considerable interest to both graduate students and researchers working to develop personalized healthcare applications, as well as physiological sensor software and hardware developers.
Photoplethysmography: Technology, Signal Analysis, and Applications is the first comprehensive volume on the theory, principles, and technology (sensors and electronics) of photoplethysmography (PPG). It provides a detailed description of the current state-of-the-art technologies/optical components enabling the extreme miniaturization of such sensors, as well as comprehensive coverage of PPG signal analysis techniques including machine learning and artificial intelligence. The book also outlines the huge range of PPG applications in healthcare, with a strong focus on the contribution of PPG in wearable sensors and PPG for cardiovascular assessment. - Presents the underlying principles and technology surrounding PPG - Includes applications for healthcare and wellbeing - Focuses on PPG in wearable sensors and devices - Presents advanced signal analysis techniques - Includes cutting-edge research, applications and future directions
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Pervasive Cardiac and Respiratory Monitoring Devices: Model-Based Design is the first book to combine biomedical instrumentation and model-based design. As the scope is limited to cardiac and respiratory devices only, this book offers more depth of information on these devices; focusing in on signals used for home monitoring and offering additional analysis of these devices. The author offers an insight into new industry and research trends, including advances in contactless monitoring of breathing and heart rate. Each chapter presents a section on current trends. As instrumentation as a field is becoming increasingly smart, basic signal processing is also discussed. Real case-studies for each modelling approach are used, primarily covering blood pressure, ECG and radar-based devices. This title is ideal for teaching and supporting learning as it is written in an accessible style and a solutions manual for the problem sets is provided. It will be useful to 4th year undergraduate students, graduate/masters/PhD students, early career researchers and professionals working on an interdisciplinary project; as it introduces the field and provides real world applications. For engineers this book solves the problem of how to assess and calibrate a medical device to ensure the data collected is trustworthy. For students, this book allows for trying concepts and circuits via simulations and learning modeling techniques. Students will learn concepts from this book and be ready to design bioinstrumentations devices based on specifications/requirements. - Focuses on model-based design using Simscape/MATLAB; learn how to design a system and how to evaluate how different choices affect the output of the system - Covers pervasive monitoring: shows how to design optimal solutions for pervasive and personalized healthcare monitoring - Explores uncertainty and sensitivity analysis; understand your model better
A signal quality assessment scheme for the photoplethysmogram waveform recorded by a pulse oximeter has been created. The signal quality algorithm uses statistical methods on time-series and spectral analysis to locate high-frequency segments of the photoplethysmogram waveform. A photoplethysmogram pulse onset detector has been implemented for heart rate estimation. Application of the signal quality metric and photoplethysmogram pulse onset detector are demonstrated in an algorithm which suppresses false electrocardiogram critical arrhythmia alarms issued by bedside monitors in hospital intensive care units.
Design of Pulse Oximeters describes the hardware and software needed to make a pulse oximeter, and includes the equations, methods, and software required for them to function effectively. The book begins with a brief description of how oxygen is delivered to the tissue, historical methods for measuring oxygenation, and the invention of the pulse oximeter in the early 1980s. Subsequent chapters explain oxygen saturation display and how to use an LED, provide a survey of light sensors, and review probes and cables. The book closes with an assessment of techniques that may be used to analyze pulse oximeter performance and a brief overview of pulse oximetry applications. The book contains useful worked examples, several worked equations, flow charts, and examples of algorithms used to calculate oxygen saturation. It also includes a glossary of terms, instructional objectives by chapter, and references to further reading.
Atrial Fibrillation from an Engineering Perspective provides an up-to-date overview of techniques developed for acquisition, modeling, and analysis of noninvasive, bioelectrical signals reflecting this common arrhythmia. Special emphasis is put on emerging technologies for monitoring of atrial fibrillation in connection with ischemic stroke, interventional ablation procedures, and pharmacological treatment, applications which all depend on the availability of techniques for detecting and characterizing episodes of paroxysmal atrial fibrillation. Detectors exploring both rhythm and morphology are described, as well as detectors confined to rhythm and better suited for low power implementation. A wide variety of approaches to modeling and characterization of atrial activity are described, emanating from a statistical and deterministic starting points. This book is suitable for graduate students, researchers, and engineers who want a comprehensive treatise of atrial fibrillation from an engineering perspective. It may be used for self-study, as a supplement to courses in signal processing, or as a modern monograph by researchers in the field of atrial fibrillation.
The book focuses on the integration of intelligent communication systems, control systems, and devices related to all aspects of engineering and sciences. It includes high-quality research papers from the 3rd international conference, ICICCD 2018, organized by the Department of Electronics, Instrumentation and Control Engineering at the University of Petroleum and Energy Studies, Dehradun on 21–22 December 2018. Covering a range of recent advances in intelligent communication, intelligent control and intelligent devices., the book presents original research and findings as well as researchers’ and industrial practitioners’ practical development experiences of.
One promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases.
