# US 12,599,309 B2 · Method and device for determining volemic status and vascular tone

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- Number: US 12,599,309 B2
- Document ID: US12599309B2
- Status: Granted
- Kind: B2
- Application number: 17/995,596
- Filed: 2021-04-08
- Published: 2023-05-18
- Granted: 2026-04-14
- Earliest claimed: 2020-04-08
- Assignees: Conscientus Aps
- Applicants: Loewenstein Medical Technology S.A.
- Inventors: Peter Kremeier; Gerardo Tusman
- Web page: https://patentgrep.com/patent/US12599309B2

## CPC

- A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)}
- A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance}
- A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction}
- A61B 5/0295: using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
- A61B 5/1495: Calibrating or testing of in-vivo probes
- A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)}
- A61B 5/6826: {Finger}
- A61B 5/7221: {Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217)}
- A61B 5/7239: {using differentiation including higher order derivatives}
- A61B 5/7246: {using correlation, e.g. template matching or determination of similarity}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms}
- A61B 5/02108: {from analysis of pulse wave characteristics}
- A61B 5/0245: by using sensing means generating electric signals, {i.e. ECG signals}
- A61B 5/0836: {Measuring rate of CO2 production}
- A61B 5/14551: {for measuring blood gases}
- A61B 5/6814: {Head}
- A61B 2560/0223: of calibration, e.g. protocols for calibrating sensors
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- A61B 2562/0247: Pressure sensors
- A61B 2562/0271: Thermal or temperature sensors
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

Method and apparatus for determining the volemic status and vascular tone of the haemodynamic system. The method comprises: receiving a photoplethysmography (PPG) signal by a computer, which signal comprises an alternating AC component as PPG amplitude and the DC component as PPG baseline from a sensor in data communication with a living tissue; determining, in each case by a computer, a multiplicity of PPG signals from the living tissue; determining a multiplicity of AC components of the PPG signals, determining a multiplicity of DC components of the PPG signals, determining a multiplicity of AC waveforms from the AC components and identifying differences in the AC waveforms over time by comparing at least two AC waveforms; determining a DC signal trend over time by comparing at least two DC components; and determining the volemic status and/or vascular tone of the haemodynamic system of the living tissue as a reaction to at least one AC waveform difference and/or a DC signal trend over time.

## Claims

### Claim 1

A method for determining a volemic status and a vascular tone of the hemodynamic system, wherein the method comprises:
(i) sensory detection of a photoplethysmography (PPG) signal from a living tissue, the PPG signal having an alternating current (AC) component as a PPG amplitude and a direct current (DC) component as a PPG baseline,
(ii) determining a multiplicity of PPG signals from the living tissue;
(iii) determining a multiplicity of AC components of the PPG signals,
(iv) determining a multiplicity of DC components of the PPG signals,
(v) determining a multiplicity of AC waveforms from the AC components, and
(vi) identifying differences in the AC waveforms over time by comparing at least two of the AC waveforms,
(vii) determining a DC signal trend over time by comparing at least two of the DC components; and
(viii) determining the vascular tone and the volemic status of the hemodynamic system of the living tissue as a reaction to at least one of the AC waveform difference and/or the DC signal trend over time, respectively,
and wherein a computer analyzes the AC components of the multiplicity of PPG signals as a measure for the vascular tone and determines a shift of the DC components of the multiplicity of PPG signals over time as a measure for the volemic status.

### Claim 2 (depends on 1)

The method of claim 1, wherein
(i) to (v), (vii) and (viii) are carried out by the computer.

### Claim 3 (depends on 1)

The method of claim 1, wherein the computer analyzes the multiplicity of PPG signals using a first or second derivative (d2DVP/dt2) of the PPG.

### Claim 4 (depends on 1)

The method of claim 1, wherein the computer analyzes the multiplicity of PPG signals using artificial neural networks, an extraction of periodic components using frequency analyses or nonlinear dynamic analyses.

### Claim 5 (depends on 1)

The method of claim 1, wherein a determination of a pulse frequency from one of the multiplicity of AC components comprises:
identifying a multiplicity of signal peaks within the one AC component by the computer;
identifying a time event for each of the multiplicity of signal peaks by the computer; and
determining a multiplicity of time differences by the computer, each time difference being determined from a first time event of a first peak value of the multiplicity of signal peaks and a second time event of a second peak value of the multiplicity of signal peaks.

### Claim 6 (depends on 1)

The method of claim 1, wherein a determination of a pulse strength metric comprises:
identifying a multiplicity of signal peaks within the multiplicity of AC components by the computer; and identifying an amplitude for each of the multiplicity of signal peaks by the computer.

### Claim 7 (depends on 6)

The method of claim 6, wherein the method further comprises a determination of an average amplitude for at least some of the multiplicity of amplitudes by the computer.

### Claim 8 (depends on 1)

The method of claim 1, wherein the computer analyzes a waveform of at least one of the multiplicity of AC components in order to identify at least one or more of a wave amplitude as a wave spacing from bottom to top, expressed in a scale from 0-100%, a systolic forward wave, a diastolic backward wave, a dichrotic notch which is determined by analysis of a first derivative of at least one of the multiplicity of PPG signals and which separates the systolic forward wave from the diastolic backward wave.

### Claim 9 (depends on 1)

The method of claim 1, wherein the computer predicts the vascular tone of the hemodynamic system from a waveform of at least one of the multiplicity of AC components, a normal vascular tone being characterized by a waveform with a certain amplitude, with a dichrotic notch being located at between 15-50% of the wave amplitude.

### Claim 10 (depends on 1)

The method of claim 1, wherein the computer uses a second PPG sensor in data communication with the living tissue, with the second PPG sensor being arranged at a different point in the living tissue in comparison with a first sensor in order to validate data from the first sensor; and/or the computer uses at least one other sensor and such sensor data in order to validate data from the first sensor; and/or the computer uses at least one other sensor and such sensor data in order to provide complementary information for a prediction of a state of the hemodynamic system.

### Claim 11 (depends on 1)

The method of claim 1, wherein the computer is configured to verify a predicted volemia by virtue of including hemodynamic parameters from other sensors; and/or
to verify a predicted vascular tone by virtue of including hemodynamic parameters from other sensors; and/or
to carry out a PPG contour analysis which identifies wave amplitude and dichrotic notch position;
capturing changes in the multiplicity of DC components in relation to a baseline;
determining a specific hemodynamic condition on the basis of results of an analysis;
and calculating an amplitude and notch position and a shift of the multiplicity of DC components from the baseline for each pulse waveform; and/or
to determine whether there is a relationship between the AC and the DC component of the PPG signal; and/or
to calibrate the PPG signal by determining the multiplicity of PPG signals over time while the living tissue is arranged at heart level for a first time period, followed by a second time period, in which the living tissue is arranged above heart level, and followed by a third time period, in which the living tissue is arranged below heart level; and/or
to calibrate the PPG signal by determining the multiplicity of PPG signals over time while the living tissue is arranged at heart level for a first time period following a second time period and/or a third time period; and/or
to calibrate the PPG signal by determining the multiplicity of PPG signals over time, determining a multiplicity of AC components of the PPG signals and determining a multiplicity of DC components of the PPG signals while the living tissue is arranged level with a heart for a first time interval,
followed by a second time interval in which the living tissue is arranged above heart level and followed by a third time interval in which the living tissue is arranged below heart level, wherein the computer stores values of PPG signals, including AC and DC components, from the first time interval, from the second time interval and the third time interval in order to determine a clinical range of normal values and highest-possible and lowest-possible limit values of the PPG signals, including AC and DC components, in relation to the vascular tone and the volemic status for a certain patient.

### Claim 12 (depends on 11)

The method of claim 11, wherein the computer is configured to perform the calibration of the PPG signal by determining the multiplicity of PPG signals over time for the first period, followed by the second period and followed by the third period and stores values of PPG signals from the first time period as base values and values from the second time period as lower limit for vasodilation and/or hypovolemia and values from the third time period as upper limit for vasoconstriction and/or hypervolemia for a certain patient.

### Claim 13 (depends on 11)

The method of claim 11, wherein the computer is configured to perform the calibration of the PPG signal by determining the multiplicity of PPG signals over time for the first period, followed by the second period and followed by the third period and the first, second and third time periods have the same length or wherein each of the first, second and third time periods takes as long as required to obtain a stable PPG signal.

### Claim 14 (depends on 1)

The method of claim 1, wherein the computer is configured to apply a rule for alerting or warning medical staff on the basis of an actual prediction of the volemic status and/or the vascular tone of the hemodynamic system, the medical staff being alerted if the volemic status and/or the vascular tone increases or decreases by a predetermined relative or absolute value.

### Claim 15 (depends on 1)

The method of claim 1, wherein the method is carried out during at least one obligatory respiratory cycle, with the computer being part of a medical ventilator or being connected to a medical ventilator in order to assess a cardiopulmonary interaction induced by the ventilator.

### Claim 16 (depends on 1)

The method of claim 1, wherein the computer predicts the vascular tone of the hemodynamic system from a waveform of at least one of the multiplicity of AC components, with a vasoconstriction being characterized by a low waveform amplitude in relation to a normal PPG amplitude, a dichrotic notch being located at above 50% of the waveform amplitude.

### Claim 17 (depends on 1)

The method of claim 1, wherein the computer predicts the vascular tone of the hemodynamic system from a waveform of at least one of the multiplicity of AC components, with a vasoconstriction being characterized by a low waveform amplitude without dichrotic notch or being merged into the systolic pulse wave(s).

### Claim 18 (depends on 1)

The method of claim 1, wherein the computer predicts the vascular tone of the hemodynamic system from a waveform of at least one of the multiplicity of AC components, with a normal vasodilation being characterized by a high waveform amplitude with a dichrotic notch being below 15% of a waveform amplitude or even below zero.

### Claim 19 (depends on 1)

The method of claim 1, wherein the computer predicts the volemic status of the hemodynamic system from the shift of the multiplicity of DC components over time, with hypervolemia being predicted if the shift of the multiplicity of DC components is beyond a defined baseline over time.

### Claim 20 (depends on 1)

The method of claim 1, wherein the computer predicts the volemic status of the hemodynamic system from the shift of the multiplicity of DC components over time, with hypovolemia being predicted if there is a shift of the multiplicity of DC components is below a defined baseline over time.