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

Sleep stage classification using hydraulic bed sensor

Sleep monitoring can help physicians diagnose and treat sleep disorders. Polysomnography(PSG) system is the most accurate and comprehensive method widely used in sleep labs to monitor sleep. However, it is expensive and not comfortable, patients have to wear numerous devices on their body surface. So a non-invasive hydraulic bed sensor has been developed to monitor sleep at home. In this thesis, the sleep stage classification problem using hydraulic bed sensor was proposed. The sleep process divided into three classes, awake, rapid eye movement (REM) and non-rapid eye movement (NREM). The ground truth sleep stage came from regularly scheduled PSG studies conducted by a sleep-credentialed physician at the Sleep Center at the Boone Hospital Center (BHC) in Columbia, Missouri. And we were allowed to install our hydraulic bed sensors to their study protocol for consenting patients. The heart rate variability (HRV) features, respiratory rate (RV) features, and linear frequency cepstral coefficient(LFCC) were extracted from the bed sensors' signals. In this study, two scenarios were applied, put all subjects together and leave one subject out. In each scenario, two types of classification structures were implemented, a single classifier and a multi-layered hierarchical method. The results show both potential benefits and limitations for using the hydraulic bed sensors to classify sleep stages.Includes bibliographical reference

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

Assessment of trends in the cardiovascular system from time interval measurements using physiological signals obtained at the limbs

Cardiovascular diseases are an increasing source of concern in modern societies due to their increasing prevalence and high impact on the lives of many people. Monitoring cardiovascular parameters in ambulatory scenarios is an emerging approach that can provide better medical access to patients while decreasing the costs associated to the treatment of these diseases. This work analyzes systems and methods to measure time intervals between the electrocardiogram (ECG), impedance plethysmogram (IPG), and the ballistocardiogram (BCG), which can be obtained at the limbs in ambulatory scenarios using simple and cost-effective systems, to assess cardiovascular intervals of interest, such as the pulse arrival time (PAT), pulse transit time (PTT), or the pre-ejection period (PEP). The first section of this thesis analyzes the impact of the signal acquisition system on the uncertainty in timing measurements in order to establish the design specifications for systems intended for that purpose. The minimal requirements found are not very demanding yet some common signal acquisition systems do not fulfill all of them while other capabilities typically found in signal acquisition systems could be downgraded without worsening the timing uncertainty. This section is also devoted to the design of systems intended for timing measurements in ambulatory scenarios according to the specifications previously established. The systems presented have evolved from the current state-of-the-art and are designed for adequate performance in timing measurements with a minimal number of active components. The second section is focused on the measurement of time intervals from the IPG measured from limb to limb, which is a signal that until now has only been used to monitor heart rate. A model to estimate the contributions to the time events in the measured waveform of the different body segments along the current path from geometrical properties of the large arteries is proposed, and the simulation under blood pressure changes suggests that the signal is sensitive to changes in proximal sites of the current path rather than in distal sites. Experimental results show that the PAT to the hand-to-hand IPG, which is obtained from a novel four-electrode handheld system, is correlated to changes in the PEP whereas the PAT to the foot-to-foot IPG shows good performance in assessing changes in the femoral PAT. Therefore, limb-to-limb IPG measurements significantly increase the number of time intervals of interest that can be measured at the limbs since the signals deliver information from proximal sites complementary to that of other measurements typically performed at distal sites. The next section is devoted to the measurement of time intervals that involve different waves of the BCG obtained in a standing platform and whose origin is still under discussion. From the relative timing of other physiological signals, it is hypothesized that the IJ interval of the BCG is sensitive to variations in the PTT. Experimental results show that the BCG I wave is a better surrogate of the cardiac ejection time than the widely-used J wave, which is also supported by the good correlation found between the IJ interval and the aortic PTT. Finally, the novel time interval from the BCG I wave to the foot of the IPG measured between feet, which can be obtained from the same bathroom scale than the BCG, shows good performance in assessing the aortic PAT. The results presented reinforce the role of the BCG as a tool for ambulatory monitoring since the main time intervals targeted in this thesis can be obtained from the timing of its waves. Even though the methods described were tested in a small group of subjects, the results presented in this work show the feasibility and potential of several time interval measurements between the proposed signals that can be performed in ambulatory scenarios, provided the systems intended for that purpose fulfill some minimal design requirements.Les malalties cardiovasculars són una tema de preocupació creixent en societats modernes, degut a l’augment de la seva prevalença i l'elevat impacte en les vides dels pacients que les sofreixen. La mesura i monitoratge de paràmetres cardiovasculars en entorns ambulatoris és una pràctica emergent que facilita l’accés als serveis mèdics i permet reduir dràsticament els costos associats al tractament d'aquestes malalties. En aquest treball s’analitzen sistemes i mètodes per la mesura d’intervals temporals entre l’electrocardiograma (ECG), el pletismograma d’impedància (IPG) i el balistocardiograma (BCG), que es poden obtenir de les extremitats i en entorns ambulatoris a partir de sistemes de baix cost, per tal d’avaluar intervals cardiovasculars d’interès com el pulse arrival time (PAT), pulse transit time (PTT) o el pre-ejection period (PEP). En la primera secció d'aquesta tesi s’analitza l’impacte del sistema d’adquisició del senyal en la incertesa de mesures temporals, per tal d’establir els requeriments mínims que s’han de complir en entorns ambulatoris. Tot i que els valors obtinguts de l’anàlisi no són especialment exigents, alguns no són assolits en diversos sistemes habitualment utilitzats mentre que altres solen estar sobredimensionats i es podrien degradar sense augmentar la incertesa en mesures temporals. Aquesta secció també inclou el disseny i proposta de sistemes per la mesura d’intervals en entorns ambulatoris d’acord amb les especificacions anteriorment establertes, a partir de l’estat de l’art i amb l’objectiu de garantir un correcte funcionament en entorns ambulatoris amb un nombre mínim d’elements actius per reduir el cost i el consum. La segona secció es centra en la mesura d’intervals temporals a partir de l’IPG mesurat entre extremitats, que fins al moment només s’ha fet servir per mesurar el ritme cardíac. Es proposa un model per estimar la contribució de cada segment arterial per on circula el corrent a la forma d’ona obtinguda a partir de la geometria i propietats físiques de les artèries, i les simulacions suggereixen que la senyal entre extremitats és més sensible a canvis en arteries proximals que en distals. Els resultats experimentals mostren que el PAT al hand-to-hand IPG, obtingut a partir d’un innovador sistema handheld de quatre elèctrodes, està fortament correlacionat amb els canvis de PEP, mentre que el PAT al foot-to-foot IPG està correlat amb els canvis en PAT femoral. Conseqüentment, l’ILG entre extremitats augmenta de manera significativa els intervals d’interès que es poden obtenir en extremitats degut a que proporciona informació complementària a les mesures que habitualment s’hi realitzen. La tercera secció està dedicada a la mesura d’intervals que inclouen les ones del BCG vertical obtingut en plataformes, de les que encara se’n discuteix l’origen. A partir de la posició temporal relativa respecte altres ones fisiològiques, s’hipostatitza que l’interval IJ del BCG es sensible a variacions del PTT. Els resultats experimentals mostren que la ona I del BCG és un millor indicador de l’ejecció cardíaca que el pic J, tot i que aquest és el més utilitzat habitualment, degut a la bona correlació entre l’interval IJ i el PTT aòrtic. Finalment, es presenta un mètode alternatiu per la mesura del PTT aòrtic a partir de l’interval entre el pic I del BCG i el peu del foot-to-foot IPG, que es pot obtenir de la mateixa plataforma que el BCG i incrementa la robustesa de la mesura. Els resultats presentats reforcen el paper del BCG com a en mesures en entorns ambulatoris, ja que els principals intervals objectiu d’aquesta tesi es poden obtenir a partir de les seves ones. Tot i que els mètodes descrits han estat provats en grups petits de subjectes saludables, els resultats mostren la viabilitat i el potencial de diversos intervals temporals entre les senyals proposades que poden ésser realitzats en entorns ambulatoris, sempre que els sistemes emprats compleixin els requisits mínims de disseny.Postprint (published version

Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation

Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards

A Low-Cost Tonometer Alternative: A Comparison Between Photoplethysmogram and Finger Ballistocardiogram and Validation Against Tonometric Waveform

Hypertension is a silent killer and one-third of its sufferers are unaware of its presence. Tonometric devices, like SphygmoCor, Compilor etc., represent the gold standard in pulse wave velocity (PWV) and augmentation index (AIx) measurements which are limited by their high cost and operational accuracy. Here, we present an alternative technology that is low cost and may be suitable for the 'wearable' setting. We undertook the comparisons of arterial waveforms obtained by photoplethysmogram (PPG) and finger ballistocardiogram (BPP) sensors which were then validated against a SphygmoCor tonometric device. Specifically, the agreement analysis of the augmentation, stiffness, reflection, elasticity, ejection elasticity and dicrotic reflection indexes showed that arterial distension waveform sensing using BPP sensor, has precision and accuracy similar to that of a SphygmoCor tonometric device whilst outperforming the volumetric arterial flow sensing using a PPG sensor, in every index. BPP indexes showed the r 2 fit of up to 0.95 and Spearman's rank correlation up to 0.91 when validated against the SphygmoCor tonometer. The estimated individual transfer functions for the BPP sensor, with reference to SphygmoCor, have accuracies of above 85% and 98% for 2 and 4-element windkessel (WK) models, respectively. The findings reported in this work may also be useful for the development of systems that are beneficial in the early and/or routine detection of hypertension

Usefulness of a lifestyle intervention in patients with cardiovascular disease

The importance of modifying lifestyle factors in order to improve prognosis in cardiac patients is well-known. Current study aims to evaluate the effects of a lifestyle intervention on changes in lifestyle- and health data derived from wearable devices. Cardiac patients from Spain (n = 34) and The Netherlands (n = 36) were included in the current analysis. Data were collected for 210 days, using the Fitbit activity tracker, Beddit sleep tracker, Moves app (GPS tracker), and the Careportal home monitoring system. Locally Weighted Error Sum of Squares regression assessed trajectories of outcome variables. Linear Mixed Effects regression analysis was used to find relevant predictors of improvement deterioration of outcome measures. Analysis showed that Number of Steps and Activity Level significantly changed over time (F = 58.21, p &lt; 0.001; F = 6.33, p = 0.01). No significant changes were observed on blood pressure, weight, and sleep efficiency. Secondary analysis revealed that being male was associated with higher activity levels (F = 12.53, p &lt; 0.001) and higher number of steps (F = 8.44, p &lt; 0.01). Secondary analysis revealed demographic (gender, nationality, marital status), clinical (co-morbidities, heart failure), and psychological (anxiety, depression) profiles that were associated with lifestyle measures. In conclusion results showed that physical activity increased over time and that certain subgroups of patients were more likely to have a better lifestyle behaviors based on their demographic, clinical, and psychological profile. This advocates a personalized approach in future studies in order to change lifestyle in cardiac patients.</p

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