A unified methodology for heartbeats detection in seismocardiogram and ballistocardiogram signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets (p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found (p < 0.01) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

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

Unen mittaaminen voimasensoreilla

This thesis presents methods for comfortable sleep measurement at home. Existing medical sleep measurement systems are costly, disturb sleep quality, and are only suited for short-term measurement. As sleeping problems are affecting about 30% of the population, new approaches for everyday sleep measurement are needed. We present sleep measurement methods that are based on measuring the body with practically unnoticeable force sensors installed in the bed. The sensors pick up forces caused by heartbeats, respiration, and movements, so those physiological parameters can be measured. Based on the parameters, the quality and quantity of sleep is analyzed and presented to the user. In the first part of the thesis, we propose new signal processing algorithms for measuring heart rate and respiration during sleep. The proposed heart rate detection method enables measurement of heart rate variability from a ballistocardiogram signal, which represents the mechanical activity of the heart. A heartbeat model is adaptively inferred from the signal using a clustering algorithm, and the model is utilized in detecting heartbeat intervals in the signal. We also propose a novel method for extracting respiration rate variation from a force sensor signal. The method solves a problem present with some respiration sensors, where erroneous cyclicity arises in the signal and may cause incorrect measurement. The correct respiration cycles are found by filtering the input signal with multiple filters and selecting correct results with heuristics. The accuracy of heart rate measurement has been validated with a clinical study of 60 people and the respiration rate method has been tested with a one-person case study. In the second part of the thesis, we describe an e-health system for sleep measurement in the home environment. The system measures sleep automatically, by uploading measured force sensor signals to a web service. The sleep information is presented to the user in a web interface. Such easy-to-use sleep measurement may help individuals to tackle sleeping problems. The user can track important aspects of sleep such as sleep quantity and nocturnal heart rate and learn how different lifestyle choices affect sleep.Unen mittaaminen voimasensoreilla Noin joka kolmannella on ongelmia unen kanssa. Nukahtamisvaikeus, heräily, huono unen laatu sekä erilaiset unenaikaiset hengitysongelmat ovat yleisiä. Helppo ja mukava unen seuranta voisi auttaa unenlaadun parantamisessa. Nykyiset mittausmenetelmät ovat kuitenkin epämukavia ja suunniteltu lähinnä lääketieteellisten diagnoosien tekemiseen. Ne eivät siis sovellu unen mittaamiseen itsenäisesti kotona. Tämä väitöskirja esittelee uuden mittausmenetelmän, joka mahdollistaa unen määrän sekä laadun mittaamisen helposti omassa sängyssä. Lakanan alle laitetaan pehmeästä kalvosta tehty anturi, joka mittaa nukkujan liikkeitä, sydämen sykettä sekä hengitystä. Anturi tunnistaa näiden mittausten perusteella useita uneen liittyviä asioita, kuten unenmäärä, kuorsaaminen sekä yön aikana mitattu leposyke. Uni-informaatio näytetään laitteen käyttäjälle verkkopalvelun tai mobiililaitteen avulla. Väitöskirjassa esitellyn unenmittausmenetelmän etu on, että syke- ja hengitystieto saadaan mitattua siitä huolimatta että anturi ei ole suoraan kosketuksissa nukkujan kehon kanssa. Kehitetyt signaalinkäsittelymenetelmät pystyvät erottamaan signaalista sykkeen ja hengityksen, sillä erilaisten mittaushäiriöiden ilmaantuminen signaaliin on otettu huomioon. Uutta unimittausmenetelmää on ehditty jo soveltaa käytännössä. Kehitetty tuote toimii siten, että mittaus lähetetään sensorilta langattomasti mobiililaitteelle, jossa unitiedot näytetään käyttäjälle. Mobiilisovellus antaa ohjeita unen parantamiseksi mittausten sekä käyttäjän profiilin perusteella

Biomedical and Human Factors Requirements for a Manned Earth Orbiting Station

This report is the result of a study conducted by Republic Aviation Corporation in conjunction with Spacelabs, Inc.,in a team effort in which Republic Aviation Corporation was prime contractor. In order to determine the realistic engineering design requirements associated with the medical and human factors problems of a manned space station, an interdisciplinary team of personnel from the Research and Space Divisions was organized. This team included engineers, physicians, physiologists, psychologists, and physicists. Recognizing that the value of the study is dependent upon medical judgments as well as more quantifiable factors (such as design parameters) a group of highly qualified medical consultants participated in working sessions to determine which medical measurements are required to meet the objectives of the study. In addition, various Life Sciences personnel from NASA (Headquarters, Langley, MSC) participated in monthly review sessions. The organization, team members, consultants, and some of the part-time contributors are shown in Figure 1. This final report embodies contributions from all of these participants

Quality of heart rate variability features obtained from ballistocardiograms

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringDavid E. ThompsonHeartbeat intervals (HBIs) vary over time, and that variance can be quantified as heart rate variability (HRV). HRV has several health-related applications including long-term health monitoring and sleep quality assessment. The focus of this research is obtaining HRV from ballistocardiograms (BCGs), force signals caused by micro-movements of the human body in response to blood ejections. This method of HRV estimation is attractive because it does not require direct attachment of any sensor to the body. However, the HBIs and corresponding HRV measured with BCGs are different than those obtained via electrocardiograms (ECGs), signals obtained by attaching electrodes to the body to detect electrical heart activity. Because ECG-based HRV is typically considered ground truth, differences in BCG-based versus ECG-based parameters are referred to as HBI and HRV errors. This research investigates the effects of HBI error on HRV feature quality. While a few studies have used BCG-based HBIs to estimate HRV features for sleep staging, the effects of HBI error on the quality of the resulting HRV features seem to have been overlooked. As a result, an acceptable HBI error range has not been defined. One contribution of this work is the development of such an acceptable error range. This dissertation work (i) develops a hardware and software system necessary to record BCGs and to perform BCG peak detection to obtain HBIs with the least possible error, (ii) determines an allowable range for HBI error by studying the effects of this error on HRV quality in the context of HRV-based sleep staging, and (iii) compares the determined acceptable HBI error range to the HBI error of our final system. The inherent error in BCG-based HBI determination due to physiological and platform effects is also taken into account in this comparison. A minimum HBI error of 20 ms was obtained from the system developed in (i), and the allowable error range was determined to be 30 ms based on the investigations conducted in (ii). The combined physiological and platform effects led to an error of 8.8 ms on average. Based on the comparisons conducted in (iii), the developed system is suitable for long-term sleep quality assessment. In addition, the effects of the HBI errors introduced by this system on the resulting HRV features are negligible in the sleep staging context

SAGA: Smart gateway for adaptive environments

The development of adaptive environments has the main objective of providing well-being to an individual, improving the environmental conditions of indoor environments and facilitating/automating any activity. In order to implement such systems, the use of devices capable of intercommunication and acquisition of environment-related parameters around the user is essential. Using wireless sensor networks, it is possible to monitor the various quality indices of indoor environments that can be used to develop strategies to improve quality of life of the users in personalized way. In this dissertation, a system based on a wireless sensor network that analyses and improves the environmental quality of indoor spaces, as well as evaluating the health status of an individual is presented. The system acquires and acts upon air quality and illumination quality-related parameters, as well as physiological data of a user, using sensor nodes and actuators distributed throughout the environment. Several wireless communication protocols have been implemented to enable intercommunication between the several elements present in the sensor network, such as actuators, sensor nodes and a coordinating / gateway node. Several warning mechanisms have been configured to alert the user to the presence of factors that may endanger their health, namely the presence of pollutants and thermal conditions that may trigger respiratory distress. In order to provide real-time system control including additional warning mechanisms, data analysis, a dedicated web application has been developed for this system. The user can control the environment according with his own needs and preferences through profiles configuration. The whole process of system development, hardware, software, experimental tests and contributions are included in this dissertation.A criação de ambientes adaptativos tem o principal objetivo de providenciar o bem-estar a um indivíduo, melhorar as condições do ambiente em seu redor e de facilitar/automatizar qualquer atividade. De forma a implementar tais sistemas, a utilização de dispositivos com capacidade de intercomunicação e de recolha de parâmetros relacionados com o ambiente em redor do utilizador é essencial. Com a utilização de redes de sensores sem fios, é possível monitorizar os diversos índices de qualidade de um ambiente interior e dessa forma melhorar a qualidade de vida. Nesta dissertação será apresentado um sistema baseado numa rede de sensores sem fios que permite analisar e melhorar a qualidade ambiental de espaços interiores e avaliar o estado de saúde de um indivíduo. O sistema adquire e atua sobre parâmetros relacionados com a qualidade do ar e qualidade de iluminação, assim como dados fisiológicos de um utilizador, através da utilização de nós de sensores e atuadores distribuídos pelo ambiente. Foram implementados diversos protocolos de comunicação sem fios para possibilitar a intercomunicação com outros elementos da rede, nomeadamente o nó coordenador/gateway. Foram configurados diversos mecanismos de alerta de forma a avisar o utilizador para a presença de fatores que possam colocar em risco a sua saúde, nomeadamente a presença de poluentes e condições térmicas que possam desencadear desconforto respiratório. De forma a proporcionar uma análise de dados em tempo real, controlo do sistema e dispor de mecanismos de alerta adicionais, foi desenvolvida uma aplicação Web dedicada a este sistema. Através desta, o utilizador poderá tornar o ambiente adaptável às suas características e de acordo com as suas preferências, através da configuração de perfis. Todo o processo de desenvolvimento do sistema, hardware, software, testes experimentais e contribuições serão incluídos nesta dissertação

Unobtrusive ballistocardiography using an electromechanical film to obtain physiological signals from children with autism spectrum disorder

Master of ScienceDepartment of Electrical and Computer EngineeringSteven WarrenPolysomnography is a method to obtain physiological signals from individuals with potential sleep disorders. Such physiological data, when acquired from children with autism spectrum disorders, could allow caregivers and child psychologists to identify sleep disorders and other indicators of nighttime well-being that affect their quality of life and ability to learn. Unfortunately, traditional polysomnography is not well suited for children with autism spectrum disorder because they commonly have an aversion to unfamiliar objects – in this case, the numerous wires and electrodes required to perform a full polysomnograph. Therefore, an innovative, unobtrusive method for gathering relevant physiological data must be designed. This report discusses several methods for obtaining a ballistocardiogram (BCG), which is a representation of the ballistic forces created by the heart during the cardiac cycle. A ballistocardiograph design is implemented using an electromechanical film placed under the center of a bed sheet. While an individual sleeps on the bed, the circuitry attached to the film extract and amplify the BCG data, which are then streamed to a computer through a LabVIEW interface and stored in a text file. These data are analyzed with a MATLAB algorithm which uses autocorrelation and linear predictive coding in the time domain to sharpen the signal. Frequency-domain peaks are then extracted to determine average heart rate every ten seconds. Initial tests involved four participants (student members of the research team) who laid in four positions: on their back, stomach, right side, and left side, yielding 16 unique data sets. Each participant laid in at least one position that allowed for accurate tracking of heart rate, with seven of the 16 signals demonstrating heart rates with less than 2% error when compared to heart rates acquired with a commercial pulse oximeter. The stomach position appeared to offer the lowest total error, while lying on the right side offered the highest total error. Overall, heart rates acquired from this initial set of participants exhibited an average error of approximately 2.5% for all four positions

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