The Influence of Timing on the Hemodynamic Effects of Compression Devices and Development of Sensor Driven Timing Mechanisms

Long periods of reduced mobility are associated with formation of blood clots in the deep veins of the legs, referred to as deep vein thrombosis (DVT). DVT has been noted as a large factor of morbidity and mortality in clinical settings as the clots can move from the legs to the lungs causing blockages, known as pulmonary embolisms. Preventing venous stasis has been clinically linked to preventing DVT formation. Venous stasis can be prevented by applying active mechanical compressions to the lower limbs, such as with an intermittent pneumatic compression (IPC) device; these devices have been clinically shown to reduce venous stasis and thereby prevent DVT formation. The main objectives of this thesis were to assess and improve a custom cardiac gated compression (CGC) system that times compressions based on an individual’s heartrate so as to only apply compressions during the diastolic phase of the cardiac cycle. A comparative performance assessment was done by measuring the central and peripheral hemodynamic changes induced by the use of different IPC devices (ArjoHuntleigh Flowtron ACS800 (Flowtron), Kendall SCD Express (SCD), Aircast Venaflow Elite (VenaFlow), and the custom CGC system) on 12 healthy human subjects in both seated and supine positions. The four selected compression devices had similar applied pressure levels, but dramatically different application profiles with regards to timing and method of application (e.g., single uniform bladder versus sequential inflation from ankle to knee). In addition, the compression systems were delineated by “smart” versus fixed timing, with two devices employing physiological measures to adjust when compression occurred (SCD: slow inflation based on vascular refill time; CGC: rapid inflation based on cardiac gating), and two inflating at fixed intervals (VenaFlow: rapid inflation; Flowtron: slow inflation). The devices were tested for ten minutes while heart rate, stroke volume, cardiac output, calf muscle oxygenation, and femoral venous and superficial femoral arterial velocities measures were collected. From the femoral venous velocities, the velocity per minute, peak velocity during the compression period, and the displacement per compression and per hour were used as performance metrics. With respect to the peak velocity the VenaFlow resulted in higher results than all other devices; likely due to its rapid inflation characteristics and the frequency of compressions allowing pooling to occur in the legs. However, in the performance metric of average displacement per compression the Flowtron and SCD resulted in the greatest displacements; likely because of the devices’ longer inflation periods resulting in a longer increase in venous velocity per compression. However, this measure does not account for the frequency of compressions unlike the displacement per hour measurement; the displacement of venous blood per hour resulted in the CGC device performing as well as the slow inflation devices during supine and resulted in greater displacement than all other devices in the seated position. The CGC also resulted in a sustained increase on the systemic and peripheral hemodynamics in the measures of SV when seated, and arterial velocity and muscle oxygenation when seated and supine; this can potentially be attributed to the device’s cardiac gating and resultant compression frequency. Interestedly, the Flowtron and SCD’s behaviour in most measures taken at steady state were not significantly different despite the “smart” timing employed by the SCD. The current timing mechanism of the CGC device is based on the previous R-R interval of the electrocardiograph (ECG) trace and a fixed pulse wave transit time. Due to heart rate variability and changes in vascular conductance, the compressions of the CGC could be more reliably triggered based on a local measurement of the pulse arrival. To address this need, a custom pulse sensor that uses photoplethysmography (PPG) to detect the pulse in the lower limb was developed. Key problems that impact wearable PPG sensor performance are motion, the power requirements of the LEDs and the ability to work on darker skin pigmentations. The sensor design was done with the primary objective of reliably and robustly detecting the pulse wave arrival regardless of skin tone or application location, and the secondary objective of maximizing the battery life of the sensor for future potential applications in wearable technologies. The developed sensor is reflectance-based and employs 6 independently controlled light emitting diodes (LEDs) surrounding a single photodiode. The independent control enables the “best” LED configuration to be selected through an in situ calibration cycle that results in the strongest signal at the lowest power setting required for different skin pigmentations in non-motion and motion conditions. The shin, in comparison to the foot and ankle, was determined as the best measurement location in terms of motion resistance and low power requirements. Furthermore, the calibration cycle proved to effectively adapt to the underlying physiology and find the LED configuration that resulted in the strongest pulse signal at the lowest possible power setting for each individual. Therefore, as the PPG sensor was proven to work effectively from the lower limb, further improvement to the timing of compressions for the CGC device may be possible through the integration of the peripheral pulse detection sensor.

REDUCTION OF SKIN STRETCH INDUCED MOTION ARTIFACTS IN ELECTROCARDIOGRAM MONITORING USING ADAPTIVE FILTERING

Cardiovascular disease (CVD) is the leading cause of death in many regions worldwide, accounting for nearly one third of global deaths in 2001. Wearable electrocardiographic cardiovascular monitoring devices have contributed to reduce CVD mortality and cost by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that can be precursor to sudden cardiac death, and the long-term assessment/monitoring of symptoms, risk factors, and the effects of therapy. However, the effectiveness of ambulatory electrocardiography to improve the treatment of CVD can be significantly impaired by motion artifacts which can cause misdiagnoses, inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a main source of motion artifact in current ECG monitors. A promising approach to reduce motion artifact is the use of adaptive filtering that utilizes a measured reference input correlated with the motion artifact to extract noise from the ECG signal. Previous attempts to apply adaptive filtering to electrocardiography have employed either electrode deformation or acceleration, body acceleration, or skin/electrode impedance as a reference input, and were not successful at reducing motion artifacts in a consistent and reproducible manner. This has been essentially attributed to the lack of correlation between the reference input selected and the induced noise. In this study, motion artifacts are adaptively filtered by using skin strain as the reference signal. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The optical strain sensor is calibrated on animal skin samples and finally in-vivo, in terms of sensitivity and measurement range. Skin stretch induced artifacts are extracted in-vivo using adaptive filters. The system and method are tested for different individuals and under various types of ambulatory conditions with the noise reduction performance quantified

VAD in failing Fontan: simulation of ventricular, cavo-pulmonary and biventricular assistance in systolic/diastolic ventricular dysfunction and in pulmonary vascular resistance increase.

Aim: Due to the lack of donors, VADs could be an alternative to heart transplantation for Failing Fontan patients (PTs). Considering the complex physiopathology and the type of VAD connection, a numerical model (NM) could be useful to support clinical decisions. The aim of this work is to test a NM simulating the VADs effects on failing Fontan for systolic dysfunction (SD), diastolic dysfunction (DD) and pulmonary vascular resistance increase (PRI). Methods: Data of 10 Fontan PTs were used to simulate the PTs baseline using a dedicated NM. Then, for each PTs a SD, a DD and a PRI were simulated. Finally, for each PT and for each pathology, the VADs implantation was simulated. Results: NM can well reproduce PTs baseline. In the case of SD, LVAD increases the cardiac output (CO) (35%) and the arterial systemic pressure (ASP) (25%). With cavo-pulmonary assistance (RVAD) a decrease of inferior vena cava pressure (IVCP) (39%) was observed with 34% increase of CO. With the BIVAD an increase of ASP (29%) and CO (37%) was observed. In the case of DD, the LVAD increases CO (42%), the RVAD decreases the IVCP. In the case of PRI, the highest CO (50%) and ASP (28%) increase is obtained with an RVAD together with the highest decrease of IVCP (53%). Conclusions: The use of NM could be helpful in this innovative field to evaluate the VADs implantation effects on specific PT to support PT and VAD selection

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

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

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology