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

Wearable estimation of central aortic blood pressure.

Arterial hypertension affects a third of the world's population and is a significant risk factor for cardiovascular disease. Blood pressure (BP) is one of the most relevant parameters used for monitoring of possible hypertension states in patients at risk of cardiovascular disease. Hence, there exists a need for new monitoring solutions, which allow to increase the frequency between BP assessments, but also allow to reduce the level of occlusion in the attempts. Moens-Korteweg equation is among the main principles to estimate BP by dispensing of any inflatable cuff. This principle might lead to an indirect estimation of BP by measuring the time it takes the pressure pulse to propagate between two pre-established vascular points, accordingly the pulse transit time (PTT) method. This thesis proposes a wearable PTT-based method to estimate central aortic BP (CABP) and, the main milestones of this work included: proof of concept of the proposed method (pilot work), the development of a wearable device (including two stages of validation), the proposition of a miniaturized version (integrated circuit) of the analog front-end of the wearable hardware, and, the development of a novel PTT-based model (PTTBM, i.e., the mathematical relationship between measured variables and estimated BP) suitable for the proposed wearable methodology to estimate BP. The main contributions found at each milestone are presented. One of the contributions of this thesis is the use of the PTT-principle for estimating CABP instead of the peripheral BP (PBP) (as typically used in the literature). The pilot work showed the feasibility of CABP estimation from the PTT principle by using electrocardiogram (ECG) and ballistocardiogram (BCG) recordings from off-the-shelf equipment. Results showed that CABP was more correlated with the proposed methodology in comparison to all PBP variables assessed; confirming our hypothesis that the CABP is the most suitable parameter to collate through the time elapsed from ECG R-wave to the BCG J-wave. That is, considered featured time (RJ-interval) includes the time of a pulse pressure propagating at an aortic district. Bland-Altman plots showed an almost zero mean error (\u\ < 0.02mmHg) and bounded standard deviation o < 5mmHg for all systolic and mean central BP readings. Pilot work provided a landmark in order to develop a compact device that allows the integration of wireless blood pressure monitoring into a wearable system. Another contribution of this thesis is the proposition of a wearable device for PTT-computing by also including design considerations for the signal conditioning chains for ECG and BCG signals. The proposed design procedure takes care of minimizing the impact of spurious delays between physiological signals, which eventually degrade the PTT computation. Further, such a procedure could be suitable for any PTT-acquisition. Filtering with low and controlled delay is required for this biomedical application, and proposed conditioning chains provide less than 2ms group-delay, showing the effectiveness of the proposed approach. In order to provide the methodology with higher autonomy and integration, a highly miniaturized implementation of the filtering approach was also proposed. It includes the design of proposed architectures in CMOS technology to implement the particular low-delay filtering at reduced bandwidth featuring ultra-low-power characteristics. Results show that less than 2ms delay for the ECG QRS-complex can be achieved with a total current consumption of IDD = 2:1nA at VDD = 1:2V of power supply. Such development meant another significant contribution of this work in the conception of highly autonomous wearable devices for PTT acquisition. The first stage of validations on the wearable CABP estimation showed that, when considering data from one volunteer, results achieved with off-the-shelf equipment could be replicated by using a proposed wearable device, and the method could be further validated by using the wearable version. Additionally, CABP estimation from the proposed wearable device could be feasible by using three feature times (FTs) as CABP surrogates; that is, RI, RJ, and IJ intervals (from ECG and BCG wearable recordings). The first validation of the method also showed that CABP could be accurately predicted by the proposed methodology when in the order of daily calibrations are performed. The second stage of validations involved a study with a group of volunteers, and new alternatives were explored (twentyseven: nine PTTBMs along the three FTs) for the CABP estimation. We found that CABP could be accurately estimated (inside AAMI requirements) through the presented methodology by using four of the explored alternatives, whereas the RI interval, an FT lacking any PTT assessment, emerged as the best surrogate for the CABP estimation. Hence, a principle different from the traditional PTT-based method arises as a more advantageous method for the CABP estimation in the light of evidence reported in this validation, and, to our knowledge, this is the first time that CABP has been successfully estimated from a wearable device. The final significant contribution of this thesis meant the last chain-link in the process to achieve an utterly original method to estimate CABP. A novel PTTBM to estimate CABP is proposed, which uses a ow-driven two-element Windkesel network constructed from FTs extracted from the wearable recordings. When classic PTTBMs are applied, the fitting of parameters often leads to values without a physiological basis. Opposite to that in the proposed PTTBM, the parameters have a clear physiological meaning, and the parameter fitting led to values that are consistent with this meaning and more stable throughout calibrations. In conclusion, this thesis introduces a novel device that exploits an alternative and indirect method for CABP estimation. Variants of the principle used, accordingly, PTT method, have been previously explored to estimate PBP but not for central aortic BP. Additionally, the device was designed to be wearable; that is, it is attached to the clothes, causing low discomfort for the user during the measurement, thus, allowing continuous and ambulatory monitoring of aortic pressure. The developed wearable system, validated in a series of volunteers, showed promising results towards the continuous CABP monitoring.Se estima que casi un tercio de la población adulta mundial sufre de algún grado de hipertensión, siendo esto un factor de riesgo significativo para la enfermedad cardiovascular. La presión arterial (PA) es el parámetro utilizado para evaluar estos posibles estados de hipertensión; actualmente existe una necesidad de generación de nuevas tecnologías que permitan aumentar la frecuencia entre medidas de PA, pero al mismo tiempo de reducir el nivel de oclusión de éstas (técnicas aceptadas están mayoritariamente basadas en la oclusión y son de acceso limitado). El modelo Moens-Korteweg podría proveer los argumentos para la creación de nuevas técnicas para estimar la PA prescindiendo de cualquier brazalete inflable. Más específicamente, podría obtenerse una estimación indirecta de la PA a través de la medición del tiempo que tarda el pulso de presión en propagarse entre dos puntos vasculares predefinidos, método conocido como tiempo de tránsito del pulso (PTT). En la presente tesis se desarrolló un dispositivo vestible que explota este método alternativo e indirecto para la estimación de la PA pero a nivel central, es decir, busca estimar la PA en la aorta (CABP), la principal arteria de la red vascular. Para ello, los principales desarrollos de este trabajo incluyeron : prueba de concepto del método propuesto basado en PTT para estimar CABP, el desarrollo de un dispositivo vestible (incluyendo dos etapas de validaciones para la estimación de la PA), la propuesta de un circuito integrado para el hardware vestible y el desarrollo de un nuevo modelo para la estimación de la PA (PTTBM, es decir, la relación matemática que vincula las variables medidas con el hardware diseñado y la estimación de la PA). A continuación se presentan las principales contribuciones resultantes de cada frente de trabajo. Una de las contribuciones de esta tesis es el uso del principio PTT para estimar CABP en lugar de la BP periférica (PBP) (como se usa típicamente en la literatura). La prueba de concepto mostró la viabilidad de la estimación de CABP a partir del principio PTT mediante la adquisición de señales electrocardiograma (ECG) y balistocardiograma (BCG) utilizando equipos comerciales. Los resultados mostraron que CABP estaba más correlacionado con la metodología propuesta en comparación con todas las variables de PBP evaluadas; confirmando nuestra hipótesis de que la CABP sería la variable más adecuada para estimar a partir del tiempo transcurrido desde la onda R del ECG hasta la onda J del BCG. Es decir, el tiempo considerado (intervalo RJ) incluye un tiempo de propagación del pulso de presión a través de un segmento aórtico. Las gráficas de Bland-Altman mostraron un error medio casi nulo (\u\ < 0.02mmHg) y una precisión o < 5mmHg para las variables de presión sistólica y media centrales. La prueba de concepto proporcionó un hito para desarrollar un dispositivo vestible apuntando a la monitorización inalámbrica de la presión arterial en un sistema imperceptible para el usuario. Otra contribución de esta tesis es la propuesta de este dispositivo vestible para la adquisición de la PTT. El desarrollo incluye consideraciones de instrumentación necesarias para el correcto acondicionamiento de las señales ECG y BCG, de las cuales se obtiene la PTT. En particular, el procedimiento de diseño propuesto busca minimizar el impacto de los retrasos espurios entre las señales fisiológicas, que eventualmente degradan la computación de la PTT. Además, dicho procedimiento podría ser aprovechado por otros desarrolladores del método sin importar las definiciones de PTT que éstos usen. La limitación de banda con bajo retardo es necesario para esta aplicación biomédica, y el hardware de acondicionamiento propuesto proporciona menos de 2 ms de retraso en las se~nales (ECG y BCG) mientras consigue limitar sus bandas a decenas de Hz, lo que muestra la efectividad de la metodología propuesta. Adicionalmente, con el fin de proporcionar a la metodología de una mayor autonomía e integración, se propone una implementación altamente miniaturizada de la sección de filtrado con bajo retraso. Se incluye el diseño de nuevas topologías propuestas en tecnología CMOS para implementar el particular filtro de bajo retraso con reducido ancho de banda, y con características de ultra bajo consumo de potencia. El diseño integrado consigue obtener resultados similares al obtenido anteriormente (con componentes discretos) alcanzando un retraso de menos de 2 ms para el complejo QRS del ECG, pero con un consumo de IDD = 2:1 nA a un VDD = 1:2 V . Tal desarrollo significó otra contribución de este trabajo en el área de circuitos altamente autónomos para instrumentación biomédica. La primera etapa de validaciones en la estimación vestible de la CABP se basó en experimentaciones con un voluntario, mostrando que, la estimación vestible podría alcanzar los mismos resultados que los alcanzados utilizando equipos de investigación, permitiendo así avanzar en la validación del método propuesto utilizando el equipamiento vestible diseñado. Además de esto, se encontró que la estimación de CABP a partir del dispositivo vestible podría ser factible utilizando varios tiempos característicos (FT) extraídos de las señales vestibles ECG y BCG (intervalos RI, RJ e IJ) junto con un popular PTTBM. La primera validación del método también arrojó que la metodología propuesta podría estimar con precisión la CABP cuando el tiempo entre calibraciones es del orden de un día. La segunda etapa de validación implicó un estudio con un grupo de voluntarios, nuevas alternativas se exploraron esta vez (veintisiete: nueve PTTBM con tres FT) para la estimación de CABP. Descubrimos que CABP podría estimarse con precisión (dentro de los requisitos de AAMI) a través de la metodología presentada mediante el uso de cuatro de las alternativas exploradas, mientras que el intervalo RI, siendo un FT que a priori no tiene ninguna vinculación con un PTT, surge como el mejor estimador de la CABP. Se concluye entonces, que un principio diferente del método tradicional basado en PTT podría ser más ventajoso para la estimación de CABP a la luz de la evidencia encontrada en esta validación y, adicionalmente, a nuestro entender, esta es la primera vez que CABP se estima con éxito a partir de un dispositivo vestible. La contribución final de esta tesis significó el último eslabón de la cadena en el proceso de lograr un método completamente original para estimar CABP de punta a punta. Se propone un nuevo PTTBM para estimar CABP, éste es basado en una red Windkesel de dos elementos bajo una excitación de flujo. Estos elementos del PTTBM son construidos a partir de cantidades extraídas a través de procesamiento de las señales vestibles ECG y BCG. Cuando se aplican los PTTBM clásicos, el ajuste de sus parámetros (en calibración) a menudo conducen a valores sin base fisiológica, mostrando a su vez, una dispersión en sus valores a lo largo de distintas calibraciones que podrían ser inaceptables en la práctica. En contraposición, los parámetros del PTTBM propuesto convergen a cantidades con significado fisiologico claro y estable a lo largo de las calibraciones. En conclusión, esta tesis presenta un dispositivo novedoso que explota un método alternativo e indirecto para la estimación de CABP. El método propuesto es basado en la metodología de PTT, que si bien ha sido previamente explotado para estimar PBP, no se ha dirigido éste hacia el monitoreo vestible de la PA aórtica central. En este marco se desarrolla un dispositivo vestible, causando baja molestia en el usuario durante las mediciones, lo que permitiría un monitoreo continuo y ambulatorio real de la presión aórtica central. El sistema vestible desarrollado, validado en una serie de voluntarios, ha mostrado resultados prometedores hacia el monitoreo continuo de CABP

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

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

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

Artifact Noise Removal Techniques and Automatic Annotation on Seismocardiogram Using Two Tri-axial Accelerometers

Heart disease are ones of the most death causes in the world. Many studies investigated in evaluating the heart performance in order to detect cardiac diseases in the early stage. The aim of this study is to monitor the heart activities in long-term on active people to reduce the risk of heart disease. Specifically, this study investigates the motion noise removal techniques using two-accelerometer sensor system and various positions of the sensors on gentle movement and walking of subjects. The study also ends up with algorithms to detect cardiac phases and events on Seismocardiogram (SCG) based on acceleration sensors. A Wi-Fi based data acquisition system and a framework on Matlab are developed to collect and process data while the subjects are in motion. The tests include eight volunteers who have no record of heart disease. The walking and running data on the subjects are analyzed to find the minimal-noise bandwidth of the SCG signal. This bandwidth is used to design bandpass filters in the motion noise removal techniques and peak signal detection. There are three main techniques of combining data of the two sensors to mitigate the motion artifact: analog processing, digital processing and fusion processing. The analog processing comprises analog ADDER/SUBTRACTOR and bandpass filter to remove the motion before entering the data acquisition system. The digital processing processes all the data using combinations of total acceleration and z-axis only acceleration. The fusion processing automatically controls the amplification gain of the SUBTRACTOR to improve signal quality as long as a signal saturation is detected. The three techniques are tested on three placements of sensors including horizontal, vertical, and diagonal on gentle motion and walking. In general, the total acceleration and z-axis acceleration are best techniques to deal with gentle motion on all placements which improve average systolic signal-noise-ratio (SNR) around 2 times and average diastolic SNR around 3 times comparing to only one accelerometer. With walking motion, overall the ADDER and zaxis acceleration are best techniques on all placements of the sensors on the body which enhance about 7 times of average systolic SNR and about 11 times of average diastolic SNR comparing to only one accelerometer. The combination of two sensors also increases the average number of recognizable systole and diastole on walking corresponding to 71.3 % and 43.8 % comparing toiii only one sensor. Among the sensor placements, the performance of horizontal placement of the sensors is outstanding comparing with other positions on all motions. There are two detection stages to detect events in the SCG for automatic annotation. First, two algorithms including moving average threshold and interpolation are applied to locate the systolic and diastolic phases. Then, based on those identified phases, cardiac events are found in the searched intervals using two outstanding characteristics of the SCG. The two algorithms of phase detection are examined on the stationary data sets of digital processing and horizontal placement. The total acceleration of only one sensor is also calculated for comparison. With moving average threshold algorithm, the average error and missing rates of total acceleration and z-axis acceleration are 1.8 % and 2.1 % respectively which are lower than using one accelerometer (3.6 %). With interpolation algorithm, the average error and missing rates of total acceleration and z-axis acceleration are in the order of 2.3 % and 2.4 % which are still lower than one accelerometer. The average calculation time of the moving average algorithm is lower than the interpolation counterpart. The real-time mode of detection algorithms is also demonstrated on Matlab framework to prove the possibility of practical applications

Methods and Algorithms for Cardiovascular Hemodynamics with Applications to Noninvasive Monitoring of Proximal Blood Pressure and Cardiac Output Using Pulse Transit Time

Advanced health monitoring and diagnostics technology are essential to reduce the unrivaled number of human fatalities due to cardiovascular diseases (CVDs). Traditionally, gold standard CVD diagnosis involves direct measurements of the aortic blood pressure (central BP) and flow by cardiac catheterization, which can lead to certain complications. Understanding the inner-workings of the cardiovascular system through patient-specific cardiovascular modeling can provide new means to CVD diagnosis and relating treatment. BP and flow waves propagate back and forth from heart to the peripheral sites, while carrying information about the properties of the arterial network. Their speed of propagation, magnitude and shape are directly related to the properties of blood and arterial vasculature. Obtaining functional and anatomical information about the arteries through clinical measurements and medical imaging, the digital twin of the arterial network of interest can be generated. The latter enables prediction of BP and flow waveforms along this network. Point of care devices (POCDs) can now conduct in-home measurements of cardiovascular signals, such as electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram (BCG) and even direct measurements of the pulse transit time (PTT). This vital information provides new opportunities for designing accurate patient-specific computational models eliminating, in many cases, the need for invasive measurements. One of the main efforts in this area is the development of noninvasive cuffless BP measurement using patient’s PTT. Commonly, BP prediction is carried out with regression models assuming direct or indirect relationships between BP and PTT. However, accounting for the nonlinear FSI mechanics of the arteries and the cardiac output is indispensable. In this work, a monotonicity-preserving quasi-1D FSI modeling platform is developed, capable of capturing the hyper-viscoelastic vessel wall deformation and nonlinear blood flow dynamics in arbitrary arterial networks. Special attention has been dedicated to the correct modeling of discontinuities, such as mechanical properties mismatch associated with the stent insertion, and the intertwining dynamics of multiscale 3D and 1D models when simulating the arterial network with an aneurysm. The developed platform, titled Cardiovascular Flow ANalysis (CardioFAN), is validated against well-known numerical, in vitro and in vivo arterial network measurements showing average prediction errors of 5.2%, 2.8% and 1.6% for blood flow, lumen cross-sectional area, and BP, respectively. CardioFAN evaluates the local PTT, which enables patient-specific calibration and its application to input signal reconstruction. The calibration is performed based on BP, stroke volume and PTT measured by POCDs. The calibrated model is then used in conjunction with noninvasively measured peripheral BP and PTT to inversely restore the cardiac output, proximal BP and aortic deformation in human subjects. The reconstructed results show average RMSEs of 1.4% for systolic and 4.6% for diastolic BPs, as well as 8.4% for cardiac output. This work is the first successful attempt in implementation of deterministic cardiovascular models as add-ons to wearable and smart POCD results, enabling continuous noninvasive monitoring of cardiovascular health to facilitate CVD diagnosis

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 wearable heart monitor at the ear using ballistocardiogram (BCG) and electrocardiogram (ECG) with a nanowatt ECG heartbeat detection circuit

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 132-137).This work presents a wearable heart monitor at the ear that uses the ballistocardiogram (BCG) and the electrocardiogram (ECG) to extract heart rate, stroke volume, and pre-ejection period (PEP) for the application of continuous heart monitoring. Being a natural anchoring point, the ear is demonstrated as a viable location for the integrated sensing of physiological signals. The source of periodic head movements is identified as a type of BCG, which is measured using an accelerometer. The head BCG's principal peaks (J-waves) are synchronized to heartbeats. Ensemble averaging is used to obtain consistent J-wave amplitudes, which are related to stroke volume. The ECG is sensed locally near the ear using a single-lead configuration. When the BCG and the ECG are used together, an electromechanical duration called the RJ interval can be obtained. Because both head BCG and ECG have low signal-to-noise ratios, cross-correlation is used to statistically extract the RJ interval. The ear-worn device is wirelessly connected to a computer for real time data recording. A clinical test involving hemodynamic maneuvers is performed on 13 subjects. The results demonstrate a linear relationship between the J-wave amplitude and stroke volume, and a linear relationship between the RJ interval and PEP. While the clinical device uses commercial components, a custom integrated circuit for ECG heartbeat detection is designed with the goal of reducing power consumption and device size. With 58nW of power consumption, the ECG circuit replaces the traditional instrumentation amplifier, analog-to-digital converter, and signal processor with a single chip solution. The circuit demonstrates a topology that takes advantage of the ECG's characteristics to extract R-wave timings at the chest and the ear in the presence of baseline drift, muscle artifact, and signal clipping.by David Da He.Ph.D