DEVELOPMENT OF PIEZOELECTRIC SENSORS AND METHODOLOGY FOR NONINVASIVE SIMULTANEOUS DETECTION OF MULTIPLE VITAL SIGNS

The activity of piezoelectric material linked the applied electric field with the strain generated that can be translated into geometrical variations. Flexible steel substrate exhibits fascinating mechanical properties which enable their integration into the emerging field of flexible microelectronics. This work presents an extended technique based on capacitance-voltage dependency to extract the geometrical variations in thin-film piezoelectric materials deposited on a flexible steel. A 50 μm flexible steel sheet has been sandwiched by two PZT film layers, each of 2.4 μm in thickness deposited by sputtering. An aluminum layer of 370 nm has been deposited above each PZT layer to form the electrical contact. The steel sheet represents the common electrode for both PZT structures. Gamry references 3000 analyzers were used to collect the capacitance-voltage measurements then estimating the piezoelectric charge constant. Experimental work has been validated by implementing the same method on a bulk piezoelectric film. Results have shown that the measured capacitance varies by 1% due to dielectric constant voltage dependency. On the other hand, 99% of capacitance variations depend on the change in physical dimensions of the sample via the piezoelectric effect. Further to that, this thesis explores the utilization of piezoelectric-based sensors to collect a corresponding representative signal from the chest surface. The subject typically needs to hold his or her breath to eliminate the respiration effect. This work further contributes to the extraction of the corresponding representative vital signs directly from the measured respiration signal. The contraction and expansion of the heart muscles, as well as the respiration activities, will induce a mechanical vibration across the chest wall. This vibration can be converted into an electrical output voltage via piezoelectric sensors. During breathing, the measured voltage signal is composed of the cardiac cycle activities modulated along with the respiratory cycle activity. The proposed technique employs the principles of piezoelectric and signal-processing methods to extract the corresponding signal of cardiac cycle activities from a breathing signal measured in real-time. All the results were validated step by step by a conventional apparatus, with good agreement observed

ARTERIAL WAVEFORM MEASUREMENT USING A PIEZOELECTRIC SENSOR

This study aims to develop a new method to monitor peripheral arterial pulse using a PVDF piezoelectric sensor. After comparing different locations of sensor placement, a specific sensor wrap for the finger was developed. Its composition, size, and location make it inexpensive and very convenient to use. In order to monitor the effectiveness of the sensor at producing a reliable pulse waveform, a monitoring system, including the PZT sensor, ECG, pulse-oximeter, respiratory sensor, and accelerometer was setup. Signal analysis from the system helped discover that the PZT waveform is relative to the 1st derivative of the artery pressure wave. Also, the system helped discover that the first, second, and third peaks in PZT waveform represent the pulse peak, inflection point, and dicrotic notch respectively. The relationship between PZT wave and respiration was also analyzed, and, consequently, an algorithm to derive respiratory rate directly from the PZT waveform was developed. This algorithm gave a 96% estimating accuracy. Another feature of the sensor is that by analyzing the relationship between pulse peak amplitude and blood pressure change, temporal artery blood pressure can be predicted during Valsalva maneuver. PZT pulse wave monitoring offers a new type of pulse waveform which is not yet fully understood. Future studies will lead to a more broadly applied use of PZT sensors in cardiac monitoring applications

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

Detection and Assessment of Sleep-Disordered Breathing with Special Interest of Prolonged Partial Obstruction

Sleep-disordered breathing (SDB) has become more common and puts more strain on public health services than ever before. Obstructive sleep apnea (OSA) and its health consequences such as different cardiovascular diseases are nowadays well recognized. In addition to OSA, attention has recently been paid to another SDB; prolonged partial obstruction. However, it is often undiagnosed and easily left untreated because of the low number of respiratory events during polysomnography recording. This patient group has found to present with more atypical subjective symptoms than OSA patients.Polysomnography (PSG) is considered to be the gold standard in reference methods in SDB diagnostics. PSG is a demanding and laborious multichannel recording method and often requires subjects to spend one night in a sleep laboratory. There is long tradition in Finland to use mattress sensors in SDB diagnostics. Recently, smaller electromechanical film transducer (Emfit) mattresses have replaced the old Static Charge-Sensitive Bed (SCSB) mattresses. However, a proper clinical validation of Emfit mattresses in SDB diagnostics has not been carried out.In this work, the use of Emfit recording in the detection of sleep apneas, hypopneas, and prolonged partial obstruction with increased respiratory effort was evaluated. The general aim of the thesis is to develop and improve the diagnostic methods for sleep-related breathing disorders.Comparisons with both PSG with nasal pressure recording and transesophageal pressure were made. Special attention was paid to the existence of the spiking phenomenon in the Emfit mattress in relation to changes in negative intrathoracic pressure in estimating increased respiratory effort. This entails monitoring the esophageal pressure as a part of nocturnal polysomnography. The recording method is demanding and uncomfortable and is usually not used with ordinary sleep laboratory patients. Thus, reliable and easy indirect quantification methods for respiratory effort are needed in clinical work. According to the results presented in this work, the Emfit signal reveals increased respiratory effort as well as apneas/hypopneas.To find out the prevalence and consequences of prolonged partial obstruction among sleep laboratory patients was another aim of this thesis. This was done by retrospective analyses of sleep laboratory patients from one year. The prevalence of patients with prolonged partial obstruction was 11%. They were as sleepy as OSA patients, but their life quality was worse, as assessed by a survey. These results, along with the findings of the heart rate variation evaluation carried out in this thesis, suggest that prolonged partial obstruction and OSA should be considered as different entities of SDB.With the Emfit mattress sensor, the SDB types can be differentiated, which is expected to enhance the accuracy of diagnostics. However, there is increasing need for easy and cheap screening methods to evaluate nocturnal breathing. In this respect, the usability of compressed tracheal sound signal scoring in SDB screening was estimated. The method reveals apneas and hypopneas but, according to the present findings, it can also be used in the detection of prolonged partial obstruction. The findings encourage the use of compressed tracheal sound analysis in screening different SDB.The analysis of sleep recordings is still based on a doctor’s subjective and visual estimation. To date, no generally accepted and sufficiently reliable automatic analysis method exists. Robust, automatic quantification methods with easier techniques for non-invasive sleep recording would enable the analysis methods to be also used for screening purposes. In this technology-orientated world, people could take much more responsibility and take care of themselves better by following their own biosignals and by changing their health habits earlier. The need for good sleep as a necessity for good life and health is widely recognized

PVDF 필름 센서를 사용한 수면관련 호흡장애 및 수면 단계의 무구속적 모니터링

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 바이오엔지니어링전공, 2015. 8. 박광석.이 연구에서는 PVDF 필름 센서를 사용하여 무구속적으로 수면관련 호흡장애 및 수면 단계를 모니터링 할 수 있는 기법을 개발하였다. PVDF 센서는 4 x 1 배열로 구성되었으며, 센서 시스템의 총 두께는 약 1.1mm 였다. PVDF 센서는 침대보와 매트리스 사이에 위치시켜 참가자의 몸에 직접적인 접촉이 없도록 하였다. 수면무호흡증 검출 연구에는 26명의 수면무호흡증 환자와 6명의 정상인이 참가하였다. PVDF 신호의 표준편차에 근거하여 수면무호흡증 검출 방법을 개발하였고, 추정된 수면무호흡증 검출 결과를 수면 전문의의 판독 결과와 비교하였다. 추정된 결과와 판독 결과 간의 수면 무호흡-저호흡 지수 상관계수는 0.94 (p < 0.001) 이었다. 코골이 검출 연구에는 총 20명의 수면무호흡증 환자가 참가하였다. PVDF 신호를 단시간 푸리에 변환하여 얻은 파워 비율과 최대 주파수를 주파수 영역 특징으로 추출하였다. 추출된 특징들을 서포트 벡터 머신 분류기에 입력하였고, 분류기의 검출 결과에 따라 코골이 또는 코골이가 아닌 구간으로 나누었다. 제안된 방법에서 추정한 코골이 검출 결과를 정상 성인 3명의 청각 및 시각 기반 코골이 판독 결과와 비교하였다. 코골이 검출에 대한 평균 민감도 및 양성예측도는 각각 94.6% 및 97.5% 이었다. 수면 단계 검출 연구에는 11명의 정상인과 13명의 수면무호흡증 환자가 참가하였다. 렘 (REM) 수면 구간은 호흡의 주기와 그 변동률에 근거하여 추정하였다. 깸 구간은 움직임 신호에 근거하여 추정하였다. 깊은 수면 구간은 분당 호흡수의 변화폭에 기반하여 추정하였다. 각 수면 단계 검출 결과를 통합하여 최종 결과를 수면 전문의의 판독 결과와 비교하였다. 30초 단위로 수면 단계를 검출하였을 때, 평균 정확도는 71.3% 이었으며 평균 카파 값은 0.48 이었다. 연구에서 제안된 PVDF 센서와 알고리즘을 통하여 상용화된 수면 모니터링 장치에 비교할 수 있을 만한 수준의 성능을 확보하였다. 이 연구 결과가 가정환경 기반 수면모니터링 시스템의 활용도와 정확도를 높이는데 기여할 수 있을 것으로 기대한다.In this study, unconstrained sleep-related breathing disorders (SRBD) and sleep stages monitoring methods using a polyvinylidene fluoride (PVDF) film sensor were established and tested. Subjects physiological signals were measured in an unconstrained manner using the PVDF sensor during polysomnography (PSG). The sensor was comprised of a 4×1 array, and the total thickness of the system was approximately 1.1 mm. It was designed to be placed under the subjects back and installed between a bed cover and mattress. In the sleep apnea detection study, twenty six sleep apnea patients and six normal subjects participated. The sleep apnea detection method was based on the standard deviation of the PVDF signals, and the methods performance was assessed by comparing the results with a sleep physicians manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). For minute-by-minute sleep apnea detection, the method classified sleep apnea with average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. In the snoring detection study, twenty patients with obstructive sleep apnea (OSA) participated. The power ratio and peak frequency from the short-time Fourier transform were used to extract spectral features from the PVDF data. A support vector machine (SVM) was applied to the spectral features to classify the data into either snore or non-snore class. The performance of the method was assessed using manual labelling by three human observers. For event-by-event snoring detection, PVDF data that contained snoring (SN), snoring with movement (SM), and normal breathing (NB) epochs were selected for each subject. The results showed that the overall sensitivity and the positive predictive values were 94.6% and 97.5%, respectively, and there was no significant difference between the SN and SM results. In the sleep stages detection study, eleven normal subjects and thirteen OSA patients participated. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respiratory rate was chosen as an indicator for slow wave sleep (SWS) detection. The performance of the method was assessed by comparing the results with manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with average accuracy of 71.3% and kappa statistic of 0.48. The experimental results demonstrated that the performances of the proposed sleep stages and SRBD detection methods were comparable to those of ambulatory devices and the results of constrained sensor based studies. The developed system and methods could be applied to a sleep monitoring system in a residential or ambulatory environment.Chapter 1. Introduction 1 1.1. Background 1 1.2. Sleep Apnea 3 1.3. Snoring 5 1.4. Sleep Stages 8 1.5. Polyvinylidene Fluoride Film Sensor 11 1.6. Purpose 12 Chapter 2. Sleep Apnea Detection 13 2.1. Signal Acquisition System 13 2.2. Methods 16 2.2.1. Participants and PSG Data 16 2.2.2. Apneic Events Detection 18 2.2.3. Statistical Analysis 25 2.3. Results 26 2.3.1. AHI Estimation 26 2.3.2. Diagnosing Sleep Apnea 29 2.3.3. Minute-By-Minute Sleep Apnea Detection 30 2.4. Discussion 34 2.4.1. Agreement between Proposed Method and PSG 34 2.4.2. Comparisons with Previous Studies 34 2.4.3. Validation of PVDF Film Sensors 36 2.4.4. Validation of Sleep Apnea Detection Algorithm 37 Chapter 3. Snoring Detection 40 3.1. Methods 40 3.1.1. Participants and PSG Data 40 3.1.2. Feature Extraction for Snoring Event Detection 42 3.1.3. Data Selection and Reference Snoring Labelling 46 3.1.4. Snoring Event Classification Based on the SVM 48 3.2. Results 52 3.2.1. Event-By-Event Snoring Detection 52 3.2.2. Snoring Event Detection and Sleep Posture 56 3.2.3. Epoch-By-Epoch Snoring Detection 57 3.3. Discussion 59 3.3.1. Agreement between Proposed Method and Reference Snoring 59 3.3.2. Comparisons with Previous Studies 59 3.3.3. Validation of the Snoring Detection Algorithm 61 Chapter 4. Sleep Stages Detection 65 4.1. Methods 65 4.1.1. Participants and PSG Data 65 4.1.2. REM Sleep Detection 68 4.1.3. Wakefulness Detection 74 4.1.4. SWS Detection 80 4.2. Results 86 4.2.1. REM Sleep Detection 86 4.2.2. Wakefulness Detection 89 4.2.3. SWS Detection 92 4.2.4. Sleep Macro- and Microstructure Detection 94 4.3. Discussion 99 4.3.1. Agreement between Proposed Method and PSG 99 4.3.2. Comparisons with Previous Studies 99 4.3.3. Validation of Sleep Stages Detection Algorithm 102 Chapter 5. Conclusion 105 References 107 Abstract in Korean 118Docto

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

Physiological and behavior monitoring systems for smart healthcare environments: a review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressedinfo:eu-repo/semantics/publishedVersio

Unobtrusive Monitoring of Heart Rate and Respiration Rate during Sleep

Sleep deprivation has various adverse psychological and physiological effects. The effects range from decreased vigilance causing an increased risk of e.g. traffic accidents to a decreased immune response causing an increased risk of falling ill. Prevalence of the most common sleep disorder, insomnia can be, depending on the study, as high as 30 % in adult population. Physiological information measured unobtrusively during sleep can be used to assess the quantity and the quality of sleep by detecting sleeping patterns and possible sleep disorders. The parameters derived from the signals measured with unobtrusive sensors may include all or some of the following: heartbeat intervals, respiration cycle lengths, and movements. The information can be used in wellness applications that include self-monitoring of the sleep quality or it can also be used for the screening of sleep disorders and in following-up of the effect of a medical treatment. Unobtrusive sensors do not cause excessive discomfort or inconvenience to the user and are thus suitable for long-term monitoring. Even though the monitoring itself does not solve the sleeping problems, it can encourage the users to pay more attention on their sleep. While unobtrusive sensors are convenient to use, their common drawback is that the quality of the signals they produce is not as good as with conventional measurement methods. Movement artifacts, for example, can make the detection of the heartbeat intervals and respiration impossible. The accuracy and the availability of the physiological information extracted from the signals however depend on the measurement principle and the signal analysis methods used. Three different measurement systems were constructed in the studies included in the thesis and signal processing methods were developed for detecting heartbeat intervals and respiration cycle lengths from the measured signals. The performance of the measurement systems and the signal analysis methods were evaluated separately for each system with healthy young adult subjects. The detection of physiological information with the three systems was based on the measurement of ballistocardiographic and respiration movement signals with force sensors placed under the bedposts, the measurement of electrocardiographic (ECG) signal with textile electrodes attached to the bed sheet, and the measurement of the ECG signal with non-contact capacitive electrodes. Combining the information produced by different measurement methods for improving the detection performance was also tested. From the evaluated methods, the most accurate heartbeat interval information was obtained with contact electrodes attached to the bed sheet. The same method also provided the highest heart rate detection coverage. This monitoring method, however, has a limitation that it requires a naked upper body, which is not necessarily acceptable for everyone. For respiration cycle length detection, better results were achieved by using signals recorded with force sensors placed under a bedpost than when extracting the respiration information from the ECG signal recorded with textile bed sheet electrodes. From the data quality point of view, an ideal night-time physiological monitoring system would include a contact ECG measurement for the heart rate monitoring and force sensors for the respiration monitoring. The force sensor signals could also be used for movement detection

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