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

Development of a bed-based nighttime monitoring toolset

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSteven WarrenA movement is occurring within the healthcare field towards evidence-based or preventative care-based medicine, which requires personalized monitoring solutions. For medical technologies to fit within this framework, they need to adapt. Reduced cost of operation, ease-of-use, durability, and acceptance will be critical design considerations that will determine their success. Wearable technologies have shown the capability to monitor physiological signals at a reduced cost, but they require consistent effort from the user. Innovative unobtrusive and autonomous monitoring technologies will be needed to make personalized healthcare a reality. Ballistocardiography, a nearly forgotten field, has reemerged as a promising alternative for unobtrusive physiological monitoring. Heart rate, heart rate variability, respiration rate, movement, and additional hemodynamic features can be estimated from the ballistocardiogram (BCG). This dissertation presents a bed-based nighttime monitoring toolset designed to monitor BCG, respiration, and movement data motivated by the need to quantify the sleep of children with severe disabilities and autism – a capability currently unmet by commercial systems. A review of ballistocardiography instrumentation techniques (Chapter 2) is presented to 1) build an understanding of how the forces generated by the heart are coupled to the measurement apparatus and 2) provide a background of the field. The choice of sensing modalities and acquisition hardware and software for developing the unobtrusive bed-based nighttime monitoring platform is outlined in Chapters 3 and 4. Preliminary results illustrating the system’s ability to track physiological signals are presented in Chapter 5. Analyses were conducted on overnight data acquired from three lower-functioning children with autism (Chapters 6 and 9) who reside at Heartspring, Wichita, KS, where results justified the platform’s multi-sensor architecture and demonstrated the system’s ability to track physiological signals from this sensitive population over many months. Further, this dissertation presents novel BCG signal processing techniques – a signal quality index (Chapter 7) and a preprocessing inverse filter (Chapter 8) that are applicable to any ballistocardiograph. The bed-based nighttime monitoring toolset outlined in this dissertation presents an unobtrusive, autonomous, robust physiological monitoring system that could be used in hospital-based or personalized, home-based medical applications that consist of short or long-term monitoring scenarios

Video-based actigraphy for monitoring wake and sleep in healthy infants:A Laboratory Study

Prolonged monitoring of infant sleep is paramount for parents and healthcare professionals for interpreting and evaluating infants' sleep quality. Wake-sleep patterns are often studied to assess this. Video cameras have received a lot of attention in infant sleep monitoring because they are unobtrusive and easy to use at home. In this paper, we propose a method using motion data detected from infrared video frames (video-based actigraphy) to identify wake and sleep states. The motion, mostly caused by infant body movement, is known to be substantially associated with infant wake and sleep states. Two features were calculated from the video-based actigraphy, and a Bayesian-based linear discriminant classification model was employed to classify the two states. Leave-one-subject-out cross validation was performed to validate our proposed wake and sleep classification model. From a total of 11.6 h of infrared video recordings of 10 healthy term infants in a laboratory pilot study, we achieved a reliable classification performance with a Cohen's kappa coefficient of 0.733 ± 0.204 (mean ± standard deviation) and an overall accuracy of 92.0% ± 4.6%.</p

Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan

Noncontact respiration monitoring techniques in young children: A scoping review

Pediatric sleep-related breathing disorders, or sleep-disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation disorders, and sleep-related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations. PubMed and EMBASE were searched for studies researching techniques in children <12 years old. Both quantitative data and the quality of the studies were analyzed. The evaluation of study quality was conducted using the QUADAS-2 tool. A total of 19 studies were included. Techniques could be grouped into bed-based methods, microwave radar, video, infrared (IR) cameras, and garment-embedded sensors. Most studies either measured respiratory rate (RR) or detected apneas; n = 2 aimed to do both. At present, bed-based approaches are at the forefront of research in noncontact RR monitoring in children, boasting the most sophisticated algorithms in this field. Yet, despite extensive studies, there remains no consensus on a definitive method that outperforms the rest. The accuracies reported by these studies tend to cluster within a similar range, indicating that no single technique has emerged as markedly superior. Notably, all identified methods demonstrate capability in detecting body movements and RR, with reported safety for use in children across the board. Further research into contactless alternatives should focus on cost-effectiveness, ease-of-use, and widespread availability

Electroencephalogram spindle detection from simultaneous Electroencephalogram (EEG) / functional Magnetic Resonance Imaging (fMRI) data for a sleep deprivation study

Sleep deprivation is an important and common problem with many consequences for mental and physical health. A sleep deprivation study was conducted to better understand the effects of insufficient sleep on cognitive functions and recovery. Studying specific spontaneous sleep waves occurring during the recovery sleep after sleep deprivation allows us to better understand the brain mechanism during sleep and its importance. The focus of my Master project is the detection of these sleep waves on scalp Electro-EncephaloGraphy (EEG) data, especially spindles, using different automatic detection algorithms and comparing them to a visual detection. Automated detection of these typical EEG discharges is challenging because many artifacts occur when EEG data are recorded within the Magnetic Resonance Imaging (MRI) scanner, so in presence of a large magnetic field. EEG data should be carefully processed from MRI related artifacts before considering detection of sleep specific discharges. In the second section of the thesis, we investigated within the whole brain the Blood Oxygen Level Dependent (BOLD) responses, measured with functional MRI, to identify brain areas involved in the generation of sleep spindles detected from scalp EEG. Preliminary results showed that automated detection was not accurate enough, because of residual artifact and other consequences of preprocessing. Moreover, visual detection is limited by the complexity of the EEG data. Therefore, there are expected improvements in post-review of the automated detection or investigating the optimal parameters for automated methods. The first BOLD response maps showed similarity between automated and visual detection and also with a study conducted by (Manuel Schabus et al. 2007). To conclude, further investigation on automated methods must be performed to find the best compromise between visual detection, automated detection, and post-review of automated detection

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 132

This special bibliography lists 261 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1974