SYSTEMS AND METHODS FOR NON-INVASIVE BLOOD PRESSURE MEASUREMENT

• US 20170156606A1
• Filed: 12/02/2016
• Published: 06/08/2017
• Est. Priority Date: 12/02/2015
• Status: Abandoned Application

• Current Assignee: Spry Health, Inc. (Asahi Kasei Corporation)
• Original Assignee: Echo Labs, Inc. (Asahi Kasei Corporation)
• Inventors: Narayanan, Ramkrishnan, Ferber, Elad, Cobut, Pierre-Jean, Gungor, Derya Gol
• Application Number:
  US15/368,504
• Publication Number:
  US 20170156606A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0008 Temperature signals
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0064 Body surface scanning
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02141 Details of apparatus construction, e.g. pump units or housings therefor, cuff pressurising systems, arrangements of fluid conduits or circuits A61B5/02233, A61B5/0235 take precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0806 by whole-body plethysmography measuring blood flow using plethysmography A61B5/0295; impedance plethysmography A61B5/053; measuring volume of the body or parts thereof A61B5/1073
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/117 Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14535 for measuring haematocrit
• CPC Class Codes: A61B 5/14539 for measuring pH
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: A61B 5/6802 : Sensor mounted on worn items
• CPC Class Codes: A61B 5/6816 : Ear lobe
• CPC Class Codes: A61B 5/6823 : Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6829 : Foot or ankle
• CPC Class Codes: A61B 5/7203 : for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/721 : using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7221 : Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: 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
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7435 : Displaying user selection data, e.g. icons in a graphical user interface
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

Systems and methods for non-invasive blood pressure measurement are disclosed. In some embodiments, a system comprises a wearable member configured to generate first and second signals, and a blood pressure calculation system. The blood pressure calculation system a pre-processing module configured to filter noise from the signals, and a wave selection module configured to identify subsets of waves of the signals, a feature extraction module configured to generate sets of feature vectors form the subsets of waves, and a blood pressure processing module configured to calculate an arterial blood pressure value based on the sets of feature vectors and an empirical blood pressure calculation model, the empirical blood pressure calculation model configured to receive the sets of feature vectors as input values. The blood pressure calculation system further includes a communication module configured to provide a message including or being based on the arterial blood pressure value.

Claims

1. A system comprising:

a wearable member including;

an energy transmitter configured to project energy at a first wavelength and energy at a second wavelength into tissue of a user; and

an energy receiver configured to generate a first signal based on a first received portion of the energy at the first wavelength and a second signal based on a second received portion of the energy at the second wavelength, the first received portion of energy and the second received portion of energy each being received through the tissue of the user; and

a blood pressure calculation system including;

a pre-processing module configured to filter noise from the first signal and the second signal;

a wave selection module configured to identify a first subset of waves from a first set of waves of the first signal and a second subset of waves from a second set of waves of the second signal, each of the first subset of waves representing a separate approximation of an average of the first set of waves over a predetermined amount of time and each of the second subset of waves representing a separate approximation of an average of the second set of waves over the predetermined amount of time;

a feature extraction module configured to generate a first set of feature vectors and a second set of feature vectors, the first set of feature vectors generated from the first subset of waves, the second set of feature vectors generated from the second subset of waves, wherein each of the feature vectors of the first set of feature vectors and the second set of feature vectors include measurement values and metric values, the measurement values corresponding to amplitude or location points of a particular wave, the metric values generated from metric functions that use at least one of the measurement values;

a blood pressure processing module configured to calculate an arterial blood pressure value based on the first set of feature vectors, the second set of feature vectors, and an empirical blood pressure calculation model, the empirical blood pressure calculation model configured to receive the first set of feature vectors and the second set of feature vectors as input values; and

a communication module configured to provide a message including or being based on the arterial blood pressure value.

2. The system of claim 1, wherein the pre-processing module is configured to filter noise from the first signal and second signal comprises the pre-processing module configured to filter noise using an adaptive filter configured to remove motion noise from the first and second signals.

3. The system of claim 1, wherein the energy transmitter includes a first light source and a second light source, the first light source configured to project the energy at the first wavelength, the second light source configured to project the energy at the second wavelength.

4. The system of claim 3, wherein the first light source and the second light source are spaced at a predetermined distance from each other, and each of the first and second light sources are associated with a different corresponding photodiode energy receiver.

5. The system of claim 4, wherein the measurement values comprise a transit time determined based on a time for blood to transit the predetermined distance between the first and second light sources.

6. The system of claim 3, wherein the measurement values include any of wave peak locations or amplitudes, or wave valley locations or amplitudes.

7. The system of claim 3, wherein the measurement values include any of an associated wave'"'"'s first or higher order derivative peak locations or amplitudes, the associated wave'"'"'s first or higher order derivative valley locations or amplitudes, or first or higher order moments of the associated wave.

8. The system of claim 3, wherein the metric functions include one or more particular metric functions that calculate a distance between two measurement values.

9. The system of claim 3, wherein the energy projected by the first light source and the energy projected by second light source each have the same wavelength.

10. The system of claim 7, wherein the feature extraction module is further configured to:

determine a phase shift between the first signal and the second signal;

calculate, based on the phase shift, any of a pulse wave velocity or a pulse transit time based on the predetermined distance; and

the blood pressure calculation module is further configured to calculate the arterial blood pressure value based on first set of feature vectors, the second set of feature vectors, any of the pulse wave velocity or the pulse transit time, the empirical blood pressure calculation model, the empirical blood pressure calculation model further configured to receive the first set of feature vectors, the second set of feature vectors, and any of the pulse wave velocity or the pulse transit time as input.

11. The system of claim 1, wherein the first signal and the second signal each comprise a photoplethysmogram (PPG) signal.

12. A method comprising:

projecting, at an energy transmitter, energy at a first wavelength and energy at a second wavelength into tissue of a user;

generating, at the energy transmitter, a first signal based on a first received portion of the energy at the first wavelength and a second signal based on a second received portion of the energy at the second wavelength, the first received portion of energy and the second received portion of energy each being received through the tissue of the user;

filtering, at the blood pressure calculation system, noise from the first signal and second signal;

identifying, at a blood pressure calculation system, a first subset of waves from a first set of waves of the first signal and a second subset of waves from a second set of waves of the second signal, each of the first subset of waves representing a separate approximation of an average of the first set of waves over a predetermined amount of time and each of the second subset of waves representing a separate approximation of an average of the second set of waves over the predetermined amount of time;

generating, at the blood pressure calculation system, a first set of feature vectors and a second set of feature vectors, the first set of feature vectors generated from the first subset of waves, the second set of feature vectors generated from the second subset of waves, wherein each of the feature vectors of the first set of feature vectors and the second set of feature vectors include measurement values and metric values, the measurement values corresponding to amplitude or location points of a particular wave, the metric values generated from metric functions that use at least one of the measurement values;

calculating, at the blood pressure calculation system, an arterial blood pressure value based on the first set of feature vectors, the second set of feature vectors, and an empirical blood pressure calculation model, the empirical blood pressure calculation model configured to receive the first set of feature vectors and the second set of feature vectors as input values; and

providing, from the blood pressure calculation system, a message including or being based on the arterial blood pressure value.

13. The method of claim 12, wherein the filtering noise from the first signal and second signal comprises filtering noise from the first signal and second signal using an adaptive filter configured to remove motion noise from the first signal and the second signal.

14. The method of claim 12, wherein the energy transmitter includes a first light source and a second light source, the first light source configured to project the energy at the first wavelength, the second light source configured to project the energy at the second wavelength.

15. The method of claim 14, wherein the first light source and the second light source are spaced at a predetermined distance from each other, and each of the first and second light sources are associated with a different corresponding photodiode energy receiver.

16. The method of claim 15, wherein the measurement values comprise a transit time determined based on a time for blood to transit the predetermined distance between the first and second light sources.

17. The method of claim 14, wherein the measurement values include any of wave peak locations or amplitudes, or wave valley locations or amplitudes.

18. The method of claim 14, wherein the measurement values include any of an associated wave'"'"'s first or higher order derivative peak locations or amplitudes, the associated wave'"'"'s first or higher order derivative valley locations or amplitudes, or first or higher order moments of the associated wave.

19. The method of claim 14, wherein the metric functions include one or more particular metric functions that calculate a distance between two measurement values.

20. The method of claim 14, wherein the energy projected by the first light source and the energy projected by second light source each have the same wavelength.

21. The method of claim 18, wherein the feature extraction module is further configured to:

determine a phase shift between the first signal and the second signal;

calculate, based on the phase shift, any of a pulse wave velocity or a pulse transit time based on the predetermined distance; and

the blood pressure calculation module is further configured to calculate the arterial blood pressure value based on first set of feature vectors, the second set of feature vectors, any of the pulse wave velocity or the pulse transit time, the empirical blood pressure calculation model, the empirical blood pressure calculation model further configured to receive the first set of feature vectors, the second set of feature vectors, and any of the pulse wave velocity or the pulse transit time as input.

22. A system comprising:

a processor; and

memory storing instructions that, when executed by the processor, cause the processor to;

receive a first signal and a second signal, the first signal being based on a first received portion of energy having been previously projected at a first wavelength into tissue of a user, the second signal being based on a second received portion of energy having been previously projected at a second wavelength into the tissue of the user;

filter noise from the first signal and the second signal, the noise including motion related noise;

identify a first subset of waves from a first set of waves of the first signal and a second subset of waves from a second set of waves of the second signal, each of the first subset of waves representing a separate approximation of an average of the first set of waves over a predetermined amount of time and each of the second subset of waves representing a separate approximation of an average of the second set of waves over the predetermined amount of time;

generate a first set of feature vectors and a second set of feature vectors, the first set of feature vectors generated from the first subset of waves, the second set of feature vectors generated from the second subset of waves, wherein each of the feature vectors of the first set of feature vectors and the second set of feature vectors include measurement values and metric values, the measurement values corresponding to amplitude or location points of a particular wave, the metric values generated from metric functions that use at least one of the measurement values;

calculate an arterial blood pressure value based on the first set of feature vectors, the second set of feature vectors, and an empirical blood pressure calculation model, the empirical blood pressure calculation model configured to receive the first set of feature vectors and the second set of feature vectors as input values; and

provide a message including or being based on the arterial blood pressure value.