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The ability to monitor arterial blood pressure continuously with unobtrusive body worn sensors may provide a unique and potentially valuable assessment of a patient's cardiovascular health. Pulse wave velocity (PWV) offers an attractive method to continuously monitoring blood pressure. However, PWV technologies based on timing measurements between the ECG and a distal PPG suffer from inaccuracies on mobile patients due to the confounding influence of pre-ejection period (PEP). In this paper, we presented a wearable, continuous blood pressure monitor (ViSi Mobile) that can measure and track changes in PEP. PEP is determined from precordial vibrations captured by an accelerometer coupled to the patient's sternum. The performance of the PEP measurements was evaluated on test subjects with postural change and patient activity. Results showed potential to improve cNIBP accuracy in active 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.

Pressure ulcers are a serious problem affecting over a million patients every year. Despite accepted guidelines for assessing and repositioning high-risk patients, the prevalence of pressure ulcers continues to rise. This paper presents a wearable, wireless vital sign monitor capable of continuously measuring the duration and orientation of a patient's posture throughout the patient's stay in a hospital. Patient posture is determined using a tri-axial accelerometer attached to a patient's torso. A novel set of algorithms are used to process the accelerometer signals to adaptively identify accelerometer alignment on the patient, calculate patient spine angle, and classify patient orientation. A unique pressure ulcer risk index based on these variables is presented to assess a patient's risk for developing pressure ulcers. Experimental results from an 18 subject trial are also presented.

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.

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.

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.

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.