Physiological Information Analysis Using Unobtrusive Sensors: BCG from Load-Cell Based Infants' Bed and ECG from Patch Electrode

학위논문 (박사)-- 서울대학교 대학원 : 바이오엔지니어링전공, 2016. 8. 박광석.The aging population, chronic diseases, and infectious diseases are major challenges for our current healthcare system. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, acquiring physiological information of different types has emerged as a promising interdisciplinary research area. Unobtrusive sensing techniques are instrumental in constructing a routine health management system, because they can be incorporated in daily life without confining individuals or causing any discomfort. This dissertation is dedicated to summarizing our research on monitoring of cardiorespiratory activities by means of unobtrusive sensing methods. Ballistocardiography and electrocardiography, which record the activity of the cardiorespiratory system with respect to mechanical or electrical characteristics, are both being actively investigated as important physiological signal measurement that provide the information required to monitor human health states. This research was carried out to evaluate the feasibility of new application methods of unobtrusive sensing that not been investigated significantly in previous investigations. We also tried to incorporate improvement essential for bringing these technologies to practical use. Our first device is a non-confining system for monitoring the physiological information of infants using ballistocardiography technology. Techniques to observe continuous biological signals without confinement may be even more important for infants since they could be used effectively to detect respiratory distress and cardiac abnormalities. We also expect to find extensive applications in the field of sleep research for analyzing sleep efficiency and sleep patterns of infants. Specifically, the sleep of infants is closely related to their health, growth, and development. Children who experience abnormal sleep and activity rhythms during their early infantile period are more prone to developing sleep-related disorders in late childhood, which are also more difficult to overcome. Therefore, studying their sleep characteristics is extremely important. Although ballistocardiography technology seems to represent a possible solution to overcome the limitations of conventional physiological signal monitoring, most studies investigating the application of these methods have focused on adults, and few have been focused on infants. To verify the usefulness of ballistocardiogram (BCG)-based physiological measurement in infants, we describe a load-cell based signal monitoring bed and assess an algorithm to estimate heartbeat and respiratory information. Four infants participated in 13 experiments. As a reference signal, electrocardiogram (ECG) and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiratory information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiratory information were found to have average performance errors of 2.55% and 2.66%, respectively. We believe that our experimental results verify the feasibility of BCG-based measurements in infants. Next, we developed a small, light, ECG monitoring device with enhanced portability and wearability, with software that contains a peak detection algorithm for analyzing heart rate variability (HRV). A mobile ECG monitoring system, which can assess an individuals condition efficiently during daily life activities, could be beneficial for management of their health care. A portable ECG monitoring patch with a minimized electrode array pad, easily attached to a persons chest, was developed. To validate the devices performance and efficacy, signal quality analysis in terms of robustness under motion, and HRV results obtained under stressful conditions were assessed by comparing the developed device with a commercially available ECG device. The R-peak detection results obtained with the device exhibited a sensitivity of 99.29%, a positive predictive value of 100.00%, and an error of 0.71%. The device also exhibited less motional noise than conventional ECG recording, being stable up to a walking speed of 5 km/h. When applied to mental stress analysis, the device evaluated the variation in HRV parameters in the same way as a reference ECG signal, with very little difference. Thus, our portable ECG device with its integrated minimized electrode patch carries promise as a form of ECG measurement technology that can be used for daily health monitoring. There is currently an increased demand for continuous health monitoring systems with unobtrusive sensors. All of the experimental results in this dissertation verify the feasibility of our unobtrusive cardiorespiratory activity monitoring system. We believe that the proposed device and algorithm presented here are essential prerequisites toward substantiating the utility of unobtrusive physiological measurements. We also expect this system can help users better understand their state of health and provide physicians with more reliable data for objective diagnosis.Chapter 1. Introduction 1 1.1. Cardiorespiratory signal and its related physiological information 2 1.1.1. Electrocardiogram 2 1.1.2. Ballistocardiogram 3 1.1.3. Respiration 4 1.1.4. Heart rate and breathing rate 5 1.1.5. Variability analysis of heart and respiratory rate 5 1.2. Unobtrusive sensing methods for continuous physiological monitoring 6 1.3. Outline of the dissertation 9 Chapter 2. Development of sensor device for unobtrusive physiological signal measurement 13 2.1. Unobtrusive BCG measurement device for infants health monitoring 13 2.1.1. Specifications of the device 17 2.1.2. Signal processing in hardware 18 2.1.3. Performance of the device 21 2.2. Unobtrusive ECG measurement device for health monitoring in daily life 25 2.2.1. Specifications of the device 26 2.2.2. Signal processing in hardware 28 2.2.3. Performance of the device 30 Chapter 3. Development of algorithm for physiological information analysis from unobtrusively measured signal 35 3.1. Algorithm for automatically analyzing unobtrusively measured BCG signal 35 3.1.1. Process flow of the algorithm 36 3.1.2. Performance evaluation 47 3.2. Algorithm for automatically analyzing unobtrusively measured ECG signal 57 3.2.1. Process flow of the algorithm 57 3.2.2. Performance evaluation 60 3.3. HRV analysis for processing unobtrusively measured signals 63 3.3.1. Optimum HRV algorithm selection in data missing simulation 64 3.3.2. Stress assessment using HRV parameters 67 Chapter 4. Discussion 71 Chapter 5. Conclusion 79 Reference 81 Abstract in Korean 89 Appendix 93Docto

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Robust Algorithms for Unattended Monitoring of Cardiovascular Health

Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

Development of a Portable Seat Cushion for the Estimation of Heart Rate Using Ballistocardiography

Cardiovascular diseases are a leading contributor of health problems all over the world and are the second leading cause of death. They are also the cause of significant economic burden, costing billions of dollars in healthcare every year. With an aging population, the strain on the healthcare system, both in terms of costs and care provision, is expected to worsen. Frequent cardiac assessment can provide essential information towards diagnosis, monitoring, and treatment, which can mitigate symptoms and improve health outcomes for people with conditions such as heart failure. This has led to increasing interest in cardiac assessment at home. Additionally, for some populations like people with limited mobility and older adults, long term vitals monitoring at a clinical setting is not feasible, making at-home monitoring more viable and economical. Most devices available for cardiac monitoring at home are wearables. While wearable technology can be accurate, it requires compliance and maintenance, which is not an ideal solution for all populations. For example, people who are not comfortable using wearables or people with a cognitive impairment may not want or be able to use wearables, which could exclude these user types from at home monitoring. Keeping these factors under consideration, the past decade has seen an increased interest in the development of technologies for Ambient Assisted Living (i.e., smart technologies integrated into a user's environment). These technologies have the potential for ongoing health monitoring in an unobtrusive manner. This thesis presents research into the development of a smart seat cushion for heart rate monitoring. The cushion is able to calculate the heart rate of a person seated on it by acquiring their Ballistocardiogram (BCG). BCG is a cardiovascular signal corresponding to the displacement of the body in response to the heart pumping blood at every heartbeat. The prototype seat cushion has load cells embedded inside it that sense the micromovements of the body and translate it to an electrical signal. An analog signal conditioning circuit amplifies and filters this signal to enhance the components corresponding to BCG before it is converted to digital form. A pilot study was conducted with twenty participants to acquire BCG in real-world scenarios: 1) sitting still, 2) reading, 3) using a computer, 4) watching TV, and 5) having a conversation. Heart rate was calculated using a novel algorithm based on Continuous Wavelet Transform by detecting the largest peaks (referred to as the J-peaks) in the BCG. Excluding three outliers, the algorithm is able to achieve an overall accuracy of 94.6% compared to gold standard Electrocardiography (ECG). This accuracy is observed to be as good as or better than those of existing wearable heart rate monitors. The seat cushion developed in this thesis research can serve as a portable solution for cardiac monitoring and can integrate into an ambient health monitoring system, offering continued monitoring of heart rate while requiring no perceived effort to operate it. Future work includes exploring different sensor configurations, machine learning based approaches for improving J-peaks detection, and real-time monitoring of heart rate

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

On the Development of Distributed Estimation Techniques for Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately witnessed tremendous demand, as evidenced by the increasing number of day-to-day applications. The sensor nodes aim at estimating the parameters of their corresponding adaptive filters to achieve the desired response for the event of interest. Some of the burning issues related to linear parameter estimation in WSNs have been addressed in this thesis mainly focusing on reduction of communication overhead and latency, and robustness to noise. The first issue deals with the high communication overhead and latency in distributed parameter estimation techniques such as diffusion least mean squares (DLMS) and incremental least mean squares (ILMS) algorithms. Subsequently the poor performance demonstrated by these distributed techniques in presence of impulsive noise has been dealt separately. The issue of source localization i.e. estimation of source bearing in WSNs, where the existing decentralized algorithms fail to perform satisfactorily, has been resolved in this thesis. Further the same issue has been dealt separately independent of nodal connectivity in WSNs. This thesis proposes two algorithms namely the block diffusion least mean squares (BDLMS) and block incremental least mean squares (BILMS) algorithms for reducing the communication overhead in WSNs. The theoretical and simulation studies demonstrate that BDLMS and BILMS algorithms provide the same performances as that of DLMS and ILMS, but with significant reduction in communication overheads per node. The latency also reduces by a factor as high as the block-size used in the proposed algorithms. With an aim to develop robustness towards impulsive noise, this thesis proposes three robust distributed algorithms i.e. saturation nonlinearity incremental LMS (SNILMS), saturation nonlinearity diffusion LMS (SNDLMS) and Wilcoxon norm diffusion LMS (WNDLMS) algorithms. The steady-state analysis of SNILMS algorithm is carried out based on spatial-temporal energy conservation principle. The theoretical and simulation results show that these algorithms are robust to impulsive noise. The SNDLMS algorithm is found to provide better performance than SNILMS and WNDLMS algorithms. In order to develop a distributed source localization technique, a novel diffusion maximum likelihood (ML) bearing estimation algorithm is proposed in this thesis which needs less communication overhead than the centralized algorithms. After forming a random array with its neighbours, each sensor node estimates the source bearing by optimizing the ML function locally using a diffusion particle swarm optimization algorithm. The simulation results show that the proposed algorithm performs better than the centralized multiple signal classification (MUSIC) algorithm in terms of probability of resolution and root mean square error. Further, in order to make the proposed algorithm independent of nodal connectivity, a distributed in-cluster bearing estimation technique is proposed. Each cluster of sensors estimates the source bearing by optimizing the ML function locally in cooperation with other clusters. The simulation results demonstrate improved performance of the proposed method in comparison to the centralized and decentralized MUSIC algorithms, and the distributed in-network algorith

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

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