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

Electromygraphy Signal Analysis Using Spectrogram

Electromyography (EMG) is known as complex bioelectricity signals that representing the contraction of the muscle in humanbody. The EMG signal offers useful information that can help to understand the human movement. Many techniques have been proposed by various researchers such as fast Fourier transforms (FFT). However, the technique only gives temporal information of the signal and does not suitable for EMG that consists of magnitude and frequency variation. In this paper,the analysis of EMG signal is presented using time-frequency distribution (TFD) which is spectrogram with different window size. Since the spectrogram represent the theEMG signal in time-frequency representation (TFR), it is very appropriate to analyze the signal. The EMG signals from Biceps muscle of two subjects are collected for body position of 0° and 90°. From the TFR, parameters of the signal such as instantaneous fundamental root mean square (RMS) voltage (Vrms) are estimated. To identify the suitable windows size, spectrogram with size window of 64, 256, 512 and 1024 is used to analyze the signal and the performance of the TFR are evaluated. The results show that spectrogram with window size of 512 gives optimal TFR of the EMG signals and suitable to characterize the signal

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

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

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Southwest Research Institute assistance to NASA in biomedical areas of the technology

Significant applications of aerospace technology were achieved. These applications include: a miniaturized, noninvasive system to telemeter electrocardiographic signals of heart transplant patients during their recuperative period as graded situations are introduced; and economical vital signs monitor for use in nursing homes and rehabilitation hospitals to indicate the onset of respiratory arrest; an implantable telemetry system to indicate the onset of the rejection phenomenon in animals undergoing cardiac transplants; an exceptionally accurate current proportional temperature controller for pollution studies; an automatic, atraumatic blood pressure measurement device; materials for protecting burned areas in contact with joint bender splints; a detector to signal the passage of animals by a given point during ecology studies; and special cushioning for use with below-knee amputees to protect the integrity of the skin at the stump/prosthesis interface

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data