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

Non Contact Heart Monitoring

Electrocardiograms are one of the most widely used methods for evaluating the structure-function relationships of the heart in health and disease. This book is the first of two volumes which reviews recent advancements in electrocardiography. This volume lays the groundwork for understanding the technical aspects of these advancements. The five sections of this volume, Cardiac Anatomy, ECG Technique, ECG Features, Heart Rate Variability and ECG Data Management, provide comprehensive reviews of advancements in the technical and analytical methods for interpreting and evaluating electrocardiograms. This volume is complemented with anatomical diagrams, electrocardiogram recordings, flow diagrams and algorithms which demonstrate the most modern principles of electrocardiography. The chapters which form this volume describe how the technical impediments inherent to instrument-patient interfacing, recording and interpreting variations in electrocardiogram time intervals and morphologies, as well as electrocardiogram data sharing have been effectively overcome. The advent of novel detection, filtering and testing devices are described. Foremost, among these devices are innovative algorithms for automating the evaluation of electrocardiograms. Permanenet links: Full chapter: http://www.intechopen.com/articles/show/title/non-contact-heart-monitoring Book: http://www.intechopen.com/books/show/title/advances-in-electrocardiograms-methods-and-analysi

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

Reconstructing the blood pressure waveform using a wearable photoplethysmograph sensor and hydrostatic pressure variations measured by accelerometers

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (leaves 51-54).An important part of a routine clinical examination is the assessment of the arterial blood pressure waveform. The variations in shape of the waveform indicate the presence of disease. In this work, a method is developed for the reconstruction of arterial blood pressure waveform using the signals obtained from a noninvasive wearable photoplethysmographic Ring Sensor and hydrostatic pressure variations measured by an Arm Accelerometer Sensor. A dynamic model with the Wiener model structure is used to establish the relationship between transmural pressure and photoplethysmographic signal. Tuned nonlinear dynamic model has been shown to be capable of estimating the arterial blood pressure waveform. The algorithm has been applied to experimental blood pressure measurements in a healthy subject and shown to provide accurate waveform reconstruction. As a result, the use of a wearable photoplethysmographic Ring Sensor can be extended to provide a finger arterial blood pressure waveform.by Aleksandar Marinković.S.M

A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

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

Modelling the characteristics of the baroreceptor

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of Witwatersrand in fulfilment of the requirements for the degree of Master of Science in Engineering. 2017The baroreceptor is a stretch receptor which detects changes in pressure in arterial blood vessels. Baroreceptor nerves inform the brainstem of changes in blood pressure, which then influences sympathetic and parasympathetic nervous activity to counteract that change. Due to the relationship between essential hypertension, sympathetic nervous activity and the baroreflex, there is some debate in the literature about whether the baroreflex can act as a long-term controller of blood pressure. This debate has increased in recent years, due to the high prevalence of essential hypertension in all societies and the introduction of new technologies to counteract drug-resistance hypertension. The baroreflex has become a source of debate due to the complex physiological feedback control that regulates blood pressure and due to new stimulating electrical devices, which have shown promising results in reducing drug-resistant essential hypertension. system. This is done through a literature survey extending through experimental and modelling research, where selected mathematical models of the baroreceptor are then analysed and simulated to find the best performing model, so that they may be simulated for an extended frequency response than what would be experimentally possible. The purpose of this investigation is to determine, through simulation, what the sensor static and dynamic characteristics are. Through this characterisation of the sensor behaviour of the baroreceptor in the baroreflex control loop, it is then possible to infer whether the baroreflex can act as a long-term controller of blood pressure. An overview of experimental and analytical investigations on the baroreceptor over the last 70 years is summarised. This overview includes mathematical models, which predict experimental results. A subset of four models from Srinivasen et al., Bugenhagen et al., Beard et al. and Mahdi et al. are selected. These models are implemented in MATLAB and Simulink. The parameters and experimental conditions are integrated into the Simulink models, and the simulated results are compared to the reported experimental data. In this way, each mathematical model is evaluated using secondary data for its ability to simulate the expected behaviour. Thereafter, all simulated models are compared under the same input conditions (a 0-230 mmHg step input over 12 s). These results are used to select the best performing models, based on how well they were parameterised and validated for experimental tests. The best performing models are those of Beard et al. and Bugenhagen et al. They are tested for a wide range of artificial inputs at different frequencies, with sinusoidal inputs which have periods that range from 0.1 s to 10 days and have a 100 mmHg operating point with a 1 mmHg peak amplitude. All modelling techniques studied show that the baroreceptor firing response resets due to the rate of change in strain in the visco-elastic arterial wall. Both tested model frequency responses, although parameterised for different species and for different major vessels, show high sensitivity to inputs in range from 1 s to 1 min 36 s (0.01 Hz 1Hz), and very low sensitivity for changes that are longer than 16 min 36s (0.001 Hz). This extrapolated simulation suggests a zero gain near DC. The simulated frequency response of the best performing baroreceptor models, which were validated against short-term experimental data, indicate that the baroreceptor is only able to sense changes that happen in less than 1 min 16s. The critical analysis of all the simulated baroreceptor models show that this characteristic of the baroreceptor is caused by the visco-elastic layers of the arterial wall, and is likely in all baroreceptors regardless of type or species. It also indicates that under electrical stimulation of the baroreceptor, the input signal from the electrical device bypasses the baroreceptor nerve ending (which is embedded in the arterial wall) and that the electrical signal of the baroreceptor is bypassed by the new stimulated electrical signal of the device. Furthermore, if the sensor can only detect short-term changes, then it is unlikely that the baroreceptor can inform the brainstem on longterm changes to mean arterial blood pressure. Therefore, based on the models examined in this study, this suggests that the baroreceptor is unlikely to be involved in long-term blood pressure control. This analysis of the best performing model is presented to show the limitations of the baroreflex in long term control of blood pressure. It serves as a simulated experiment to rationalise the contentious debate around the role of the baroreflex in long term blood pressure control, and to allow for future improvements that can be made on the baroreceptor model to allow for more extended modelling on sor characteristics. An improvement that could be applied to the best performing baroreceptor models, implemented in this study, is to examine the effects of ageing and inter-species variability on carotid sinus dimensions and visco-elastic wall properties.CK201

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