Methodological Role of Mathematics to Estimate Human Blood Pressure Through Biosensors

This paper presents a non-invasive technique and cuff less method for blood pressure measurement with a hardware prototype implementation. The sophisticated feature called pulse transit time (PTT) is extracted and investigated with a development of a smart system which consists of ECG, PPG sensor to estimate the systolic and diastolic blood pressure with support of advanced signal processing methodologies. The proposed method experiments have been carried out in hospital environment and tested with real time patients to validate the proposed method. The maximum error percentage of the proposed system has been shown to be 5.3% of systolic blood pressure (mmHg) and 4.7% of diastolic blood pressure (mmHg). This system also allows the monitoring of patient hypertension and overcome the limitation of cuff-based hospitalized measurement system

A NOVEL WAVEFORM MIRRORING TECHNIQUE FOR SYSTOLIC BLOOD PRESSURE ESTIMATION FROM ANACROTIC PHOTOPLETHYSMOGRAM

Continuous cuffless Blood Pressure (BP) measurement is an important tool to monitor the health of individuals at risk. In this study, a new method is proposed for Systolic BP (SBP) estimation utilizing Photoplethysmograms (PPG). To this end, toe and carotid PPG were recorded from seventeen subjects aged 20-28 years, whereas their SBP were measured using a standard BP cuff monitor for validation purpose. The proposed method is based on a novel mirroring technique, which allows for an accurate estimation of the Pulse Transit Time (PTT) from the PPG’s rising part (anacrotic) waveform using an ARX System Identification approach. Based on the modified Moens-Korteweg equation, SBP was then calculated based on the estimated PTT values obtained from the ARX model. The estimated PTT was found to be highly correlated to the measured SBP (R2 = 0.98). Comparison of calculated SBP to the measured SBP obtained using standard BP cuff monitor results in a mean error of 3.4%. Given that 95% of the estimated SBP values are accurate in the +/- 8 mmHg range, this method seems promising for non-invasive, continuous BP monitorin

Pulse transit time measured by photoplethysmography improves the accuracy of heart rate as a surrogate measure of cardiac output, stroke volume and oxygen uptake in response to graded exercise

Heart rate (HR) is a valuable and widespread measure for physical training programs, although its description of conditioning is limited to the cardiac response to exercise. More comprehensive measures of exercise adaptation include cardiac output ((Q) over dot), stroke volume (SV) and oxygen uptake ((V) over dotO(2)), but these physiological parameters can be measured only with cumbersome equipment installed in clinical settings. In this work, we explore the ability of pulse transit time (PTT) to represent a valuable pairing with HR for indirectly estimating (Q) over dot, SV and (V) over dotO(2) non-invasively. PTT was measured as the time interval between the peak of the electrocardiographic (ECG) R-wave and the onset of the photoplethysmography (PPG) waveform at the periphery (i.e. fingertip) with a portable sensor. Fifteen healthy young subjects underwent a graded incremental cycling protocol after which HR and PTT were correlated with (Q) over dot, SV and (V) over dotO(2) using linear mixed models. The addition of PTT significantly improved the modeling of (Q) over dot, SV and (V) over dotO(2) at the individual level (R-1(2) = 0.419 for SV, 0.548 for (Q) over dot, and 0.771 for (V) over dotO(2)) compared to predictive models based solely on HR (R-1(2) = 0.379 for SV, 0.503 for (Q) over dot, and 0.745 for (V) over dotO(2)). While challenges in sensitivity and artifact rejection exist, combining PTT with HR holds potential for development of novel wearable sensors that provide exercise assessment largely superior to HR monitors

Алгоритм для моніторингу артеріального тиску за часовими характеристиками пульсової хвилі

Обсяг дипломної роботи складає 79 сторінок, містить 28 ілюстрації, 10 таблиць, 51 формулу. Загалом було опрацьовано 47 літературних джерела. Актуальність: полягає у розробці та створені технології для безперервного непрямого (неінвазивного) моніторингу артеріального тиску в умовах звичайної активності пацієнтів, що забезпечувала б точність вимірювань, порівняну з традиційними методами. Мета: математична модель та алгоритм для моніторингу артеріального тиску на основі часу затримки пульсової хвилі. Завдання: –оглянути засоби вимірювання артеріального тиску; –провести аналіз механічних моделей артеріальної стінки; –побудувати математичну модель поширення пульсової хвилі; – розробка алгоритму для моніторингу та розрахунку артеріального тиску за часом затримки пульсової хвилі; –аналіз отриманих результататів.Scope of the diploma is 79 pages, contains 28 illustrations, 10 tables, 51 formulas. 47 sources were totally processed. Relevance is the need for a technology for continuous non–invasive blood pressure monitoring, applicable for daily use, which would ensure the accuracy of measurements, compared with traditional methods. Objective: algorithm for artery blood pressure monitoring based on the time characteristics of the pulse wave. Task: –analysis of blood pressure monitoring problems; –analysis of mechanical models of the arterial wall; –creation a mathematical model of the pulse wave propagation process; –development of an algorithm for blood pressure estimation using pulse wave delay; –analysis of the obtained results

Widefield Computational Biophotonic Imaging for Spatiotemporal Cardiovascular Hemodynamic Monitoring

Cardiovascular disease is the leading cause of mortality, resulting in 17.3 million deaths per year globally. Although cardiovascular disease accounts for approximately 30% of deaths in the United States, many deleterious events can be mitigated or prevented if detected and treated early. Indeed, early intervention and healthier behaviour adoption can reduce the relative risk of first heart attacks by up to 80% compared to those who do not adopt new healthy behaviours. Cardiovascular monitoring is a vital component of disease detection, mitigation, and treatment. The cardiovascular system is an incredibly dynamic system that constantly adapts to internal and external stimuli. Monitoring cardiovascular function and response is vital for disease detection and monitoring. Biophotonic technologies provide unique solutions for cardiovascular assessment and monitoring in naturalistic and clinical settings. These technologies leverage the properties of light as it enters and interacts with the tissue, providing safe and rapid sensing that can be performed in many different environments. Light entering into human tissue undergoes a complex series of absorption and scattering events according to both the illumination and tissue properties. The field of quantitative biomedical optics seeks to quantify physiological processes by analysing the remitted light characteristics relative to the controlled illumination source. Drawing inspiration from contact-based biophotonic sensing technologies such as pulse oximetry and near infrared spectroscopy, we explored the feasibility of widefield hemodynamic assessment using computational biophotonic imaging. Specifically, we investigated the hypothesis that computational biophotonic imaging can assess spatial and temporal properties of pulsatile blood flow across large tissue regions. This thesis presents the design, development, and evaluation of a novel photoplethysmographic imaging system for assessing spatial and temporal hemodynamics in major pulsatile vasculature through the sensing and processing of subtle light intensity fluctuations arising from local changes in blood volume. This system co-integrates methods from biomedical optics, electronic control, and biomedical image and signal processing to enable non-contact widefield hemodynamic assessment over large tissue regions. A biophotonic optical model was developed to quantitatively assess transient blood volume changes in a manner that does not require a priori information about the tissue's absorption and scattering characteristics. A novel automatic blood pulse waveform extraction method was developed to encourage passive monitoring. This spectral-spatial pixel fusion method uses physiological hemodynamic priors to guide a probabilistic framework for learning pixel weights across the scene. Pixels are combined according to their signal weight, resulting in a single waveform. Widefield hemodynamic imaging was assessed in three biomedical applications using the aforementioned developed system. First, spatial vascular distribution was investigated across a sample with highly varying demographics for assessing common pulsatile vascular pathways. Second, non-contact biophotonic assessment of the jugular venous pulse waveform was assessed, demonstrating clinically important information about cardiac contractility function in a manner which is currently assessed through invasive catheterization. Lastly, non-contact biophotonic assessment of cardiac arrhythmia was demonstrated, leveraging the system's ability to extract strong hemodynamic signals for assessing subtle fluctuations in the waveform. This research demonstrates that this novel approach for computational biophotonic hemodynamic imaging offers new cardiovascular monitoring and assessment techniques, which can enable new scientific discoveries and clinical detection related to cardiovascular function

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference