Devin Mccombie

Utility of the Photoplethysmogram in Circulatory Monitoring

The photoplethysmogram is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is displayed by many pulse oximeters and bedside monitors, along with the computed arterial oxygen saturation. The photoplethysmogram is similar in appearance to an arterial blood pressure waveform. Because the former is noninvasive and nearly ubiquitous in hospitals whereas the latter requires invasive measurement, the extraction of circulatory information from the photoplethysmogram has been a popular subject of contemporary research. The photoplethysmogram is a function of the underlying circulation, but the relation is complicated by optical, biomechanical, and physiologic covariates that affect the appearance of the photoplethysmogram. Overall, the photoplethysmogram provides a wealth of circulatory information, but its complex etiology may be a limitation in some novel applications.

Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics.

A method for estimating pulse wave velocity (PWV) using circulatory waveform signals derived from multiple photoplethysmograph (PPG) sensors is described. The method employs two wearable in-line PPG sensors placed at a known distance from one another at the ulnar and digital artery. A technique for calibrating the measured pulse wave velocity to arterial blood pressure using hydrostatic pressure variation is presented. Additionally, a framework is described for estimating local arterial dynamics using PPG waveforms and multi-channel blind system ID. Initial results implementing the method on data derived from a human subject at different arterial pressures is presented. Results show that the method is capable of measuring the changes in arterial PWV that result from fluctuations in mean arterial pressure

Laguerre-model blind system identification: cardiovascular dynamics estimated from multiple peripheral circulatory signals

This paper presents a method for comparing multiple circulatory waveforms measured at different locations to improve cardiovascular parameter estimation from these signals. The method identifies the distinct vascular dynamics that shape each waveform signal, and estimates the common cardiac flow input shared by them. This signal-processing algorithm uses the Laguerre function series expansion for modeling the hemodynamics of each arterial branch, and identifies unknown parameters in these models from peripheral waveforms using multichannel blind system identification. An effective technique for determining the Laguerre base pole is developed, so that the Laguerre expansion captures and quickly converges to the intrinsic arterial dynamics observed in the two circulatory signals. Furthermore, a novel deconvolution method is developed in order to stably invert the identified dynamic models for estimating the cardiac output (CO) waveform from peripheral pressure waveforms. The method is applied to experimental swine data. A mean error of less than 5% with the measured peripheral pressure waveforms has been achieved using the models and excellent agreement between the estimated CO waveforms and the gold standard measurements have been obtained.

Adaptive hydrostatic blood pressure calibration: Development of a wearable, autonomous pulse wave velocity blood pressure monitor

A technique for calibrating non-invasive peripheral arterial sensor signals to peripheral arterial blood pressure (BP) is proposed. The adaptive system identification method utilizes a measurable intra-arterial hydrostatic pressure change in the sensor outfitted appendage to identify the transduction dynamics relating the peripheral arterial blood pressure and the measured arterial sensor signal. The proposed algorithm allows identification of the calibration dynamics despite unknown physiologic fluctuations in arterial pressure during the calibration period under certain prescribed conditions. By employing unique wearable sensor architecture to estimate pulse wave velocity (PWV), this technique is used to calibrate peripheral pulse transit time measurements to arterial blood pressure. This sensor architecture is comprised of two inline photoplethysmograph sensors one in the form of a wristwatch measuring the pulse waveform in the ulnar artery and one in the form of a ring measuring the pulse waveform from the digital artery along the base of the little finger. Experimental results using the proposed algorithm to calibrate PTT to BP on human subjects will be presented.

Motion based adaptive calibration of pulse transit time measurements to arterial blood pressure for an autonomous, wearable blood pressure monitor

This paper presents a novel adaptive algorithm for calibrating non-invasive pulse transit time (PTT) measurements to arterial blood pressure (BP). This new algorithm allows complete calibration of PTT to BP without the use of an oscillometric blood pressure cuff or external pressure sensor. Further, the algorithm can be used to continually update the identified parameters in the calibration equation while the patient is wearing the device. The technique utilizes natural patient motion to generate a known change in the transmural pressure (input) acting on the arteries monitored by our device to produce a measurable change in pulse transit time (output). The natural motion includes varying the height of the sensor relative to the heart to alter hydrostatic pressure at the measurement site and adjusting proximal joint posture to vary the external arterial pressure at the measurement site. This new algorithm is applied to a unique wearable sensor architecture that combines two in-line PPG sensors, one located at the ulnar artery of the wrist and one located at the digital artery of the little finger along with a multi-axis accelerometer for height measurement. Initial human subject tests results using the new algorithm and device will be presented.

Towards the Development of Wearable Blood Pressure Sensors: A Photo-Plethysmograph Approach Using Conducting Polymer Actuators

lood pressure is among the most critical measures that physicians use for broad clinical applications. Yet, continuous blood pressure measurement is available only for hospital settings and other limited cases. As of today, there is no wearable sensor that can measure blood pressure continuously and reliably as well as for a long period of time. Bulky wrist-band type sensors and armcuff type sensors are practically infeasible to wear for a long time, due to interference with patients’ daily activities. It is expected that development of truly wearable blood pressure (BP) sensors will have a significant impact upon broad health monitoring applications. The authors’ group has been working on the development of BP sensors based on photoplethysmogram (PPG). PPG is a non-invasive circulatory signal related to the pulsatile volume of blood in tissue, displayed by most pulse-oximeters along with the computed arterial oxygen saturation. PPG is a desirable sensor modality for wearable health monitoring, since it is miniaturizable and of low power consumption [1]. Previous developments of PPG ring sensors and others have demonstrated that those devices have the potential for long-term, continuous monitoring of pulse, oxygen saturation, and pulse rate variability. It is known that PPG has a strong correlation with arterial blood pressure (ABP) waveform. The relationship between PPG and ABP, however, varies depending on a number of factors. Caution must be taken when estimating ABP from PPG signals. In this paper, first we will discuss how PPG signals are related to ABP, and which factors influence the PPG-ABP relationship and to what extent. Based on the literature of PPG as well as on our own experiments, we will investigate the relationship between the two. An effective calibration procedure will then be developed to estimate ABP continuously from wearable PPG sensors. Finally, new designs of wearable ABP sensors using conducting polymer actuators will briefly be discussed at the end.

Assessing the challenges of a pulse wave velocity based blood pressure measurement in surgical patients.

Development of a continuous noninvasive blood pressure (cNIBP) monitor that is unobtrusive to patients is an attractive alternative to the cuff based measurements performed on medical-surgical floors in the hospital. Pulse wave velocity (PWV) provides a means to continuously monitor blood pressure in these patients. However, a PWV based cNIBP monitor faces a number of challenges in order to accurately measure blood pressure. In our study, we investigated some of the challenges faced by a body-worn cNIBP monitor (i.e. ViSi Mobile) on data collected on patients undergoing surgery. Results indicated that 1) pulse arrival time (PAT) values from ViSi Mobile were well correlated with PAT values obtained from an invasive reference; 2) the reciprocal of the PAT measurements were linearly correlated with blood pressure but the calibration curve was altered by administration of certain vasoactive substances; and 3) there are deterministic correlations between systolic pressure, diastolic pressure and the corresponding mean arterial pressure over a wide range of blood pressure values.

Adaptive Left Ventricular Ejection Time Estimation Using Multiple Peripheral Pressure Waveforms

An adaptive approach is proposed for the problem of left ventricular ejection time (LVET) estimation using peripheral pressure waveform signals. The proposed algorithm, which makes use of 2 peripheral pressure measurements, makes it possible to adaptively estimate the LVET in response to different cardiovascular physiologic states. The algorithm builds on features obtained from global and branch-specific characterization of the cardiovascular circulation as well as waveform features to dramatically improve the accuracy of LVET estimation. The performance of the proposed approach is evaluated with respect to its heart-rate-based conventional counterpart, which shows approximately 40% improvement of estimation accuracy in terms of R2 values from 0.6655 for the conventional waveform-based approach to 0.9222 for the proposed approach

Identification of Vascular Dynamics and Estimation of the Cardiac Output Waveform from Wearable PPG Sensors

A method for estimating cardiovascular dynamics and cardiac output waveforms using signals derived from two PPG sensors is presented. The method employs a novel signal-processing algorithm known as Laguerre model blind system identification to identify the vascular dynamics associated with the measured PPG signals. A unique deconvolution method is then used with the identified Laguerre models to estimate the cardiac output waveform. Initial results implementing the method on data derived from a human subject is presented. Results show good agreement between the morphology of the estimated waveform and the typical morphology of the human cardiac output waveform

A critical appraisal of opportunities for wearable medical sensors

This paper provides an appraisal of the sensor requirements and prospects available for the growing field of wearable medical sensors. The results of a literature survey for various sensor use-models indicate that the design goals for each intended sensor application must focus on task specific criteria for ultimate sensor acceptance. Provided use-models include the examination of the relevant medical problems, the diagnostic utility of the available physiologic signals, and the impact of false alarms on the specific implementation area.