# US 10,226,194 B2 · Statistical, noninvasive measurement of a patient's physiological state

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- Number: US 10,226,194 B2
- Document ID: US10226194B2
- Status: Granted
- Kind: B2
- Application number: 14/867,938
- Filed: 2015-09-28
- Published: 2016-01-21
- Granted: 2019-03-12
- Earliest claimed: 2008-10-29
- Assignees: Flashback Technologies, Inc.
- Applicants: The Regents of the University of Colorado, a body corporate
- Inventors: Gregory Zlatko Grudic; Steven Lee Moulton; Isobel Jane Mulligan
- Web page: https://patentgrep.com/patent/US10226194B2

## CPC

- A61B 5/031: {Intracranial pressure}
- A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B 5/021: Measuring pressure in heart or blood vessels
- A61B 5/02108: {from analysis of pulse wave characteristics}
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)}
- A61B 5/0836: {Measuring rate of CO2 production}
- A61B 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)}
- A61B 5/14551: {for measuring blood gases}
- A61B 5/318: Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 8/488: {involving Doppler signals}
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 40/60: for the operation of medical equipment or devices
- G16H 40/63: for local operation
- G16H 50/50: for simulation or modelling of medical disorders
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- A61B 5/4261: {Evaluating exocrine secretion production (A61B3/101 takes precedence)}
- A61B 5/4839: {combined with drug delivery}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's physiological state. In an aspect, some tools and techniques can estimate predict the onset of conditions intracranial pressure, an amount of blood volume loss, cardiovascular collapse, and/or dehydration. Some tools can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

## Claims

### Claim 1

A computational method of estimating a patient's physiological state, the method comprising:
generating a model of the physiological state, wherein generating the model comprises:
receiving data pertaining to a plurality of physiological parameters of a test subject to obtain a plurality of physiological data sets;
directly measuring a physiological state of the test subject with a reference sensor to obtain a plurality of physiological state measurements; and
correlating the received data with the physiological state of the test subject, wherein correlating the received data with the measured physiological state comprises:
identifying a most-predictive set of signals Sk out of a set of signals s1, s2, . . . , sD for each of one or more outcomes ok, wherein the most-predictive set of signals Sk corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes ok represents one of the plurality of intracranial pressure measurements;
autonomously learning a set of probabilistic predictive models ôk=Mk(Sk), where ôk is a prediction of outcome ok derived from a model Mk that uses as input values obtained from the set of signals Sk; and
repeating the operation of autonomously learning incrementally from data that contains examples of values of signals s1, s2, . . . , sD and corresponding outcomes o1, o2, . . . , oK;
receiving, at a computer system, a set of input data from one or more physiological sensors, the input data pertaining to one or more physiological parameters of a patient;
analyzing, with the computer system, the input data against the model to generate diagnostic data concerning the patient's physiological state; and
displaying, with a display device, at least a portion of the diagnostic data concerning the patient's physiological state.

### Claim 2 (depends on 1)

The method of claim 1, wherein the patient's physiological state comprises intracranial pressure, and wherein the diagnostic data comprises an estimate of a current intracranial pressure of the patient.

### Claim 3 (depends on 1)

The method of claim 1, wherein the model is a hemodynamic compensation model, and wherein the patient's physiological state is a volume of acute blood loss.

### Claim 4 (depends on 3)

The method of claim 3, wherein the diagnostic data concerning the patient's physiological state comprises a prediction of when the patient will experience cardiovascular collapse.

### Claim 5 (depends on 1)

The method of claim 1, wherein the physiological state is a state of dehydration.

### Claim 6 (depends on 1)

The method of claim 1, wherein the method is noninvasive to the patient, and wherein the one or more physiological sensors are noninvasive sensors.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
controlling, with the computer system, a therapeutic treatment of the patient's physiological state, based at least in part on the generated diagnostic data.

### Claim 8 (depends on 7)

The method of claim 7, wherein controlling a therapeutic treatment comprises selecting one or more recommended treatment options, out of a plurality of treatment options.

### Claim 9 (depends on 8)

The method of claim 8, wherein controlling a therapeutic treatment further comprises administering, with the computer system, the one or more recommended treatment options.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
repeating the operation of receiving a set of input data over a plurality of iterations, to produce a plurality of sets of input data, wherein analyzing the input data comprises analyzing the plurality of sets of input data to generate a plurality of sets of diagnostic data;
wherein displaying at least a portion of the diagnostic data comprises updating a display on the display device to display at least some of the plurality of sets of diagnostic data over the period of time.

### Claim 11 (depends on 10)

The method of claim 10, wherein the plurality of sets of input data comprise periodic samples of a set of continuous, physiological waveform data.

### Claim 12 (depends on 11)

The method of claim 11, wherein the set of continuous, physiological waveform data comprises blood pressure waveform data.

### Claim 13 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise an electrocardiograph sensor.

### Claim 14 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise an impedance cardiograph sensor.

### Claim 15 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise a pulse oximeter.

### Claim 16 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise a plethysmograph.

### Claim 17 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise a photoplethysmograph (PPG).

### Claim 18 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise a near infrared sensor.

### Claim 19 (depends on 1)

The method of claim 1, wherein the one or more physiological sensors comprise a transcranial Doppler sensor.

### Claim 20 (depends on 1)

The method of claim 1, wherein autonomously learning the set of probabilistic predictive models comprises using a linear model framework to identify predictive variables for each increment of data.

### Claim 21 (depends on 20)

The method of claim 20, wherein the linear model framework is constructed with the form
o
^
k
=
f
k
⁡
(
a
0
+
∑
i
=
1
d
⁢
⁢
a
i
⁢
s
i
)
,
where fk is any mapping from one input to one output and a0, a1, . . . , ad are linear model coefficients.

### Claim 22 (depends on 1)

The method of claim 1, further comprising:
updating the model using the set of input data.

### Claim 23

An apparatus, comprising:
a non-transitory computer readable medium having encoded thereon a set of instructions executable by one or more computers to:
generate a model of the physiological state, wherein generating the model comprises:
receiving data pertaining to a plurality of physiological parameters of a test subject to obtain a plurality of physiological data sets;
directly measuring a physiological state of the test subject with a reference sensor to obtain a plurality of physiological state measurements; and
correlating the received data with the physiological state of the test subject, wherein correlating the received data with the measured physiological state comprises:
identifying a most-predictive set of signals Sk out of a set of signals s1, s2, . . . , sD for each of one or more outcomes ok, wherein the most-predictive set of signals Sk corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes ok represents one of the plurality of intracranial pressure measurements;
autonomously learning a set of probabilistic predictive models ôk=Mk(Sk), where ok is a prediction of outcome ok derived from a model Mk that uses as input values obtained from the set of signals Sk; and
repeating the operation of autonomously learning incrementally from data that contains examples of values of signals s1, s2, . . . , sD and corresponding outcomes o1, o2, . . . , oK;
receive a set of input data from one or more physiological sensors, the input data pertaining to one or more physiological parameters of a patient;
analyze the input data against the model to generate diagnostic data concerning the patient's physiological state; and
display, with a display device, at least a portion of the diagnostic data concerning the patient's physiological state.

### Claim 24

A system, comprising:
a computer system, comprising:
one or more processors; and
a non-transitory computer readable medium in communication with the one or more processors, the computer readable medium having encoded thereon a set of instructions executable by the computer system to:
generate a model of the physiological state, wherein generating the model comprises:
receiving data pertaining to a plurality of physiological parameters of a test subject to obtain a plurality of physiological data sets;
directly measuring a physiological state of the test subject with a reference sensor to obtain a plurality of physiological state measurements; and
correlating the received data with the physiological state of the test subject, wherein correlating the received data with the measured physiological state comprises:
identifying a most-predictive set of signals Sk out of a set of signals s1, s2, . . . , sD for each of one or more outcomes Ok, wherein the most-predictive set of signals Sk corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes ok represents one of the plurality of intracranial pressure measurements;
autonomously learning a set of probabilistic predictive models ôk=Mk(Sk), where ôk is a prediction of outcome ok derived from a model Mk that uses as input values obtained from the set of signals Sk; and
repeating the operation of autonomously learning incrementally from data that contains examples of values of signals s1, s2, . . . , sD and corresponding outcomes o1, o2, . . . , oK;
receive a set of input data from one or more physiological sensors, the input data pertaining to one or more physiological parameters of a patient;
analyze the input data against the model to generate diagnostic data concerning the patient's physiological state; and
display, with a display device, at least a portion of the diagnostic data concerning the patient's physiological state.

### Claim 25 (depends on 23)

The system of claim 23, further comprising the one or more physiological sensors.