Michigan State University

Massachusetts Institute of Technology

University System of Maryland

Other

Georgia Tech Research Corporation

Ramakrishna Mukkamala

Jin-Oh Hahn

Richard J Cohen

Anand Chandrasekhar

Gokul Swamy

Tongtong Li

Derin A Sherman

Mohsen Moslehpour

Keerthana Natarajan

Mingwu Gao

Jiankun Liu

Qi Ling

Omer T Inan

Mohammad Yavarimanesh

Stephanie Lind-Ober Martin

Da Xu

Chang-Sei Kim

Guanqun Zhang

Nicholas Bari Olivier

Various systems, techniques, and embodiments are disclosed for implementing a blood-pressure measurement method that does not require specialized equipment (such as inflatable blood pressure cuffs). The measurement can be taken from arterial locations within a user's finger, via the standard equipment and features of many widely-available consumer mobile devices. Such devices can be programmed to accurately and easily guide a user to press her finger on the screen of a device at a precise location, so that an accurate measurement can be taken. For example, guidance visualizations on a screen (such as finger silhouettes and animations) can be employed on the same screen on which a user presses her finger. Pressure sensitivity and optical camera readings are then used to calculate blood pressure for the user.

Mobile device applications to measure blood pressure

Current oscillometric devices for monitoring central blood pressure (BP) maintain the cuff pressure at a constant level to acquire a pulse volume plethysmography (PVP) waveform and calibrate it to brachial BP levels estimated with population average methods. A physiologic method was developed to further advance central BP measurement. A patient-specific method was applied to estimate brachial BP levels from a cuff pressure waveform obtained during conventional deflation via a nonlinear arterial compliance model. A physiologically-inspired method was then employed to extract the PVP waveform from the same waveform via ensemble averaging and calibrate it to the brachial BP levels. A method based on a wave reflection model was thereafter employed to define a variable transfer function, which was applied to the calibrated waveform to derive central BP.

Central blood pressure monitoring via a standard automatic arm cuff

A system and method is presented for cuff-less blood pressure measurement in a mobile device. A key aspect of this disclosure is the discovery of a new location for blood pressure measurement at the fingertip of a subject and that reflectance-mode photoplethysmography can be used to help make this measurement. Through experiments in human subjects, it was discovered that it is indeed possible to measure systemic blood pressure by having a subject press the fingertip against a reflectance-mode photo-plethysmography-force sensor unit under visual guidance and then compute blood pressure from the resulting variable-amplitude blood volume oscillations and applied pressure via an oscillometric algorithm.

Method and apparatus for cuff-less blood pressure measurement in a mobile device

A method and apparatus for monitoring arterial properties, including systolic and diastolic pressure levels, of a subject is provided, in which a hardware processor receives and analyzes ballistocardiogram (BCG) data of the subject. A non-transient computer readable medium, accessible by the hardware processor, contains instructions that, when executed by the hardware processor, identify features of the BCG waveform and determine the arterial properties therefrom. For example, a diastolic pressure level may be determined from a time interval between the 'I' and 'J' peaks of the waveform and a systolic pressure level determined from the amplitude difference between the &#x2018;J&#x2019; and &#x2018;K&#x2019; peaks of the waveform in combination with the &#x2018;I-J&#x2019; time interval or amplitude difference. A physical mechanism for the BCG data is disclosed that enables other arterial properties of the subject to be determined from the BCG data alone or from the BCG data in combination with other measurements.

Monitor for blood pressure and other arterial properties

Most automatic cuff blood pressure (BP) measurement devices are based on oscillometry. These devices estimate BP from the envelopes of the cuff pressure oscillations using fixed ratios. The values of the fixed ratios represent population averages, so the devices may be accurate only in subjects with normal BP levels. A patient-specific oscillometric BP measurement method was developed. The idea was to represent the cuff pressure oscillation envelopes with a physiologic model and then estimate the patient-specific parameters of the model, which includes BP levels, by optimally fitting it to the envelopes.

Method for determining cuff blood pressure

A method is provided for determining pulse transit time of a subject as a function of blood pressure. The method includes: measuring a proximal waveform indicative of the arterial pulse at a proximal site of the subject; measuring a distal waveform indicative of the arterial pulse at a distal site of the subject; defining a relationship between the proximal waveform and the distal waveform in terms of unknown parameters of a nonlinear model; determining the unknown parameters of the nonlinear model from the measured proximal waveform and the measured distal waveform; and determining pulse transit time for the subject as a function of blood pressure from the parameters of the nonlinear model. The nonlinear model can account for arterial compliance and peripheral wave reflection, where the arterial compliance depends on blood pressure.

Methods and apparatus for determining pulse transit time as a function of blood pressure

Methods are presented for determining pulse transit time (PTT) and/or pulse wave velocity (PWV) of a subject by application of parametric system identification to proximal and distal arterial waveforms. The two waveforms are measured from the subject. A system is defined that relates the proximal arterial waveform to the distal arterial waveform (or vice versa) in terms of the unknown parameters of a parametric mathematical model. The model parameters are determined from the measured waveforms using system identification. PTT between the proximal and distal arterial sites is then determined from the system model. PWV may also be determined by dividing the distance between measurement sites (D) by PTT.

Methods and apparatus for determining arterial pulse wave velocity

A method is provided for determining a central aortic pressure (AP) wave-form for a subject. The method includes measuring a peripheral artery pressure (PAP) waveform from the subject, employing a distributed model to define a pressure-to-pressure transfer function relating PAP to AP and a pressure-to-flow transfer function relating PAP to a central arterial flow in terms of the same unknown parameters, estimating the unknown parameters by finding the pressure-to-flow transfer function, which when applied to the measured PAP waveform, minimizes the magnitude of the central arterial flow waveform during diastole, and applying the pressure-to-pressure transfer function with the estimated parameters to determine an AP waveform for the subject.

Methods and apparatus for determining a central aortic pressure waveform from a peripheral artery pressure waveform

A method is provided for determining blood pressure for a subject using a sphygmomanometer. The method includes: measuring an oscillometric cuff pressure waveform of the subject using the sphygmomanometer; representing the measured waveform with a physical model accounting for mechanics of the cuff, an artery and coupling between the cuff and the artery; determining the model unknowns from the measured waveform; and determining blood pressure for the subject using the determined model.

Methods and apparatus for determining cuff blood pressure

A method is provided for determining a central aortic pressure waveform. The method includes: measuring two or more peripheral artery pressure waveforms; analyzing the signals so as to extract common features in the measured waveforms; and determining an absolute central aortic pressure waveform based on the common features.

Method and apparatus for determining a central aortic pressure waveform

The present invention provides methods and apparatus for determining a dynamical property of the systemic or pulmonary arterial tree using long time scale information, i.e., information obtained from measurements over time scales greater than a single cardiac cycle. In one aspect, the invention provides a method and apparatus for monitoring cardiac output (CO) from a single blood pressure signal measurement obtained at any site in the systemic or pulmonary arterial tree or from any related measurement including, for example, fingertip photoplethysmography. According to the method the time constant of the arterial tree, defined to be the product of the total peripheral resistance (TPR) and the nearly constant arterial compliance, is determined by analyzing the long time scale variations (greater than a single cardiac cycle) in any of these blood pressure signals. Then, according to Ohm's law, a value proportional to CO may be determined from the ratio of the blood pressure signal to the estimated time constant. The proportional CO values derived from this method may be calibrated to absolute CO, if desired, with a single, absolute measure of CO (e.g., thermodilution). The present invention may be applied to invasive radial arterial blood pressure or pulmonary arterial blood pressure signals which are routinely measured in intensive care units and surgical suites or to noninvasively measured peripheral arterial blood pressure signals or related noninvasively measured signals in order to facilitate the clinical monitoring of CO as well as TPR.

Methods and apparatus for determining cardiac output

Method and apparatus for determining central aortic pressure waveform

A method is provided for determining ejection fraction. The method includes: measuring a physiologic signal indicative of blood pressure; analyzing the physiologic signal at more than one time instance so as to extract information present in its temporal variations; and determining ejection fraction based in part on the extracted information.

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Okemos, MI, United States of America

Ramakrishna Mukkamala