Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

A Morphological Approach To Identify Respiratory Phases Of Seismocardiogram

Respiration affects the cardiovascular system significantly and the morphology of signals relevant to the heart changes with respiration. Such changes have been used to extract respiration signal from electrocardiogram (ECG). It is also shown that accelerometers placed on the body can be used to extract respiration signals. It has been demonstrated that the signal morphology for seismocardiogram, the lower frequency band of chest accelerations, is different between inhale and exhale. For instance, systolic time intervals (STI), which provide a quantitative estimation of left ventricular performance, vary between inhale and exhale phases. In other words, heart beats happening in exhale phase are different compared to those in inhale phase. Thus, our main goal in this thesis is investigating feasibility of finding an automatic morphological based method to identify respiratory phases of heart cycles. In this thesis, forty signal recordings from twenty subjects were used. In each recording, the reference respiratory belt signal, three dimensional (3D) chest acceleration signals, and electrocardiogram signals were recorded. The first stage was is choosing a proper estimated respiratory signal. The second stage, was the automatic respiratory phase detection of heart cycles using the selected estimated respiratory signal. The result shows that among estimated respiratory signals, accelerometer-derived respiration (ADR), in z-direction, has a potential m to identify respiratory phase of heart cycles with total accuracy of about 77%

Estimación robusta de la diferencia del tiempo de tránsito del pulso sanguíneo a partir de señales fotopletismográficas

En el presente trabajo se va a estudiar la posibilidad de detectar estrés mental utilizando técnicas no invasivas basadas en la señal fotopletismográfica de pulso (PPG). Para ello se pretende detectar cambios en la velocidad de pulso arterial (PWV), utilizando señales de PPG tomadas en dos puntos distintos del árbol arterial con las que poder medir el tiempo de llegada de pulso arterial a la periferia (PAT) y la diferencia de ese tiempo de llegada entre dos puntos de la periferia distintos (PTTD). Tanto el PAT como el PTTD han sido propuestas en la bibliografía como medidas influenciados por el Tiempo de Tránsito de Pulso (PTT), este último capaz de medir cambios en la dinámica cardiovascular. Sin embargo, el PTTD, al contrario que el PAT, no necesita del electrocardiograma (ECG) para ser obtenido y no está influenciado por el periodo de pre-eyección (PEP) -un intervalo de tiempo en la sístole ventricular que cambia pulso a pulso- el cual genera que el PAT pierda la relación con el PTT, dos factores importantes que aventajan al PTTD frente al PAT. Primero, se estudia de fiabilidad de los puntos fiduciales para la detección de los pulsos de la señal PPG y con ésto comprobar cuál es el método con la mayor precisión. Se demuestra mediante diversos análisis que el mejor punto para detectar los pulsos corresponde al valor de la PPG en el instante de máxima pendiente (valor máximo en la primera derivada). Resulta necesario implementar un detector de artefactos ya que el método de adquisición de la PPG es muy sensible a ellos pudiendo llegar a haber segmentos en los que la señal registrada es absolutamente inutilizable. Posteriormente, se analizan 14 voluntarios sanos sometidos a un protocolo de estrés y se realiza un test estadístico para comprobar la validez del método propuesto. Los resultados muestran que la desviación estándar de la PTTD tiene la capacidad estadística suficiente como para discernir entre estados de estrés y de relajación, para cada uno de los sujetos por separado. Además, se puede ver una tendencia descendente generalizada del descenso de la PTTD en situación de estrés con respecto a relajación. %Sin embargo, resultará necesario repetir el análisis con una muestra de señales mayor ya que se dispone de pocos sujetos en la base de datos utilizada, ya que la calidad de la señal de PPG que se registró en la frente es muy mala y hay muy pocos sujetos con los que se puede computar la PTTD. A modo de conclusión, se ha visto que la PTTD contiene información fisiológica que puede ser interesante para la detección de estrés. A su vez, también es una técnica potencialmente interesante para otros tipos de aplicaciones clínicas tales como la estimación no invasiva de la presión arterial o la evaluación de la rigidez arterial, pero se necesita estudiar la adecuación de ésta en cada escenario en particular. Además, como la PTTD se puede medir a partir de únicamente dos señales PPG, la técnica es idónea para dispositivos wearable y smartphones

Dynamic relationship between cardiac imaging and physiological measurements

PhD ThesisImpedance cardiography (ICG) is a non-invasive technique to measure the dynamic changes in electrical impedance of the thorax. Photoplethymgraphy (PPG) is an optical- based non-invasive physiological measurement technique used to detect the blood volume pulses in the microvascular bed of tissue. These two physiological measurements have potential clinical importance to enable simple and cost-efficient ways to examine cardiovascular function and provide surrogate or additional clinical information to the measures from cardiac imaging. However, because the origins of the characteristic waveforms of the impedance and pulse are still not well understood, the clinical applications of these two techniques are limited. There were two main aims in this study: 1) to obtain a better understanding of the origins of the pulsatile impedance changes and peripheral pulse by linking their characteristic features beat-by-beat to those from simultaneous echocardiograms; 2) to validate the clinical indices from ICG and PPG with those well-established echocardiographic indices. Physiological signals, including ECGs, impedance, the first derivative impedance and finger and ear pulses, were simultaneously recorded with echocardiograms from 30 male healthy subjects at rest. The timing sequence of cardiovascular events in a single cardiac cycle was reconstructed with the feature times obtained from the physiological measurements and images. The relations of the time features from the impedance with corresponding features from images and pulses were investigated. The relations of the time features from peripheral pulses with corresponding features from images were also investigated. Furthermore, clinical time indices measured from the impedance and pulse were validated with the reference to the echocardiograms. Finally, the effects of age, heart rate and blood pressure on the image and physiological measurements were examined. According to the reconstructed timing sequence, it was evident that the systolic waves of the thoracic impedance and peripheral pulse occurred following left ventricular ejection. The impedance started to fall 26 ms and the pulse arrived at the fingertip 162 ms after the aortic valve opened. A diastolic wave was observed during the ventricular passive filling phase on the impedance and pulse. The impedance started to recover during the late ventricular ejection phase when the peripheral pulse was rising up. While the pulsatile impedance changes were mainly correlated with valve movement, the derivative impedance (velocity of impedance change) was more correlated with aortic flow (velocity of blood 2 flow). The foot of the finger pulse was significantly correlated with aortic valve open (R = 0.361, P < 0.05), while its systolic peak was strongly correlated with the aortic valve 2 closing (R = 0.579, P < 0.001). Although the pulse had similar waveform shapes to the inverted impedance waveform, the associations between the time features of these two signals were weak. During the validation of potential clinical indices from ICG, significant correlation was found between the overall duration of the derivative impedance systolic wave (359 ms) and the left ventricular ejection time (LVET) measured by aortic valve open duration from M- 2 mode images (329 ms) (R = 0.324, P < 0.001). The overall duration from the finger pulse foot to notch (348 ms) was also significantly correlated with the LVET from M-mode 2 images (R = 0.461, P < 0.001). Therefore, both ICG and PPG had the potential to provide surrogates to the LVET measurement. Age influenced the cardiovascular diastolic function more than systolic function on normal subjects. With age increasing, the reduction of the left ventricular passive filling was compensated by active filling. The ratio of the passive filling duration to the active 2 filling duration decreased with age (R = 0.143, P < 0.05). The influence of age on the diastolic wave of the impedance signals was striking. The impedance diastolic wave disappeared gradually with age. The effects of age on the peripheral pulse were mainly on the shortened pulse foot transit time (PPT) and prolonged pulse rise time. The large artery f stiffness index (SI) increased with age. Most time intervals were prolonged with heart rate slowing down. The effects of systolic blood pressure were evident on pulse transit time and pulse diastolic rising time. Driven by higher systolic blood pressure, both PPT and rising f time decreased significantly (P < 0.001). In conclusion, from the analysis based on simultaneous physiological measurements and echocardiograms, both the pulsatile impedance changes and peripheral volume pulse were initiated by left ventricular ejection. The thoracic impedance changes reflected volume changes in the central great vessels, while the first derivative impedance was associated with the velocity of blood flow. Both ICG and PPG had the potential to provide surrogates for the measures of cardiac mechanical functions from images. The PPG technique also enabled the assessment of changes in vascular function caused by age.Institute of Cellular Medicine Newcastle Universit

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Enabling Wearable Hemodynamic Monitoring Using Multimodal Cardiomechanical Sensing Systems

Hemodynamic parameters such as blood pressure and stroke volume are instrumental to understanding the pathogenesis of cardiovascular disease. Unfortunately, the monitoring of these hemodynamic parameters is still limited to in-clinic measurements and cumbersome hardware precludes convenient, ubiquitous use. To address this burden, in this work, we explore seismocardiogram-based wearable multimodal sensing techniques to estimate blood pressure and stroke volume. First, the performance of a multimodal, wrist-worn device capable of obtaining noninvasive pulse transit time measurements is used to estimate blood pressure in an unsupervised, at-home setting. Second, the feasibility of this wrist-worn device is comprehensively evaluated in a diverse and medically underserved population over the course of several perturbations used to modulate blood pressure through different pathways. Finally, the ability of wearable signals—acquired from a custom chest-worn biosensor—to noninvasively quantify stroke volume in patients with congenital heart disease is examined in a hospital setting. Collectively, this work demonstrates the advancements necessary towards enabling noninvasive, longitudinal, and accurate measurements of these hemodynamic parameters in remote settings, which offers to improve health equity and disease monitoring in low-resource settings.Ph.D

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

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