Calculating Pulse Transit Time

• US 20150112606A1
• Filed: 10/23/2014
• Published: 04/23/2015
• Est. Priority Date: 10/23/2013
• Status: Abandoned Application

• Current Assignee: Robert F. Dudley
• Original Assignee: Quanttus Inc
• Inventors: He, David Da, Bijjani, Richard Robehr
• Application Number:
  US14/521,829
• Publication Number:
  US 20150112606A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02427 Details of sensor
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0285 Measuring or recording phase velocity of blood waves
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/113 occurring during breathing
• 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 5/14542 : for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/165 : Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/363 : Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4809 : Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4815 : Sleep quality
• CPC Class Codes: A61B 5/4818 : Sleep apnoea
• CPC Class Codes: A61B 5/4833 : Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/486 : Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6801 : specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6898 : Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• 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 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 3/0481 : based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G08B 21/0453 : worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 21/06 : indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The technology described in this document is embodied in a method that includes obtaining a first data set representing time-varying information on at least one pulse pressure wave at a first body part of a subject. The method also includes obtaining a second data set representing time-varying information about motion of the subject at the first body part of a subject. The method also includes identifying a first point in the first data set, the first point representing an arrival time of the pulse pressure wave at the first body part, and identifying a second point in the second dataset, the second point representing an earlier time at which the pulse pressure wave traverses a second body part of the subject. The method also includes computing a pulse transit time (PTT) as a difference between the first and second points.

Claims

1. A method comprising:

obtaining, using a first sensor, a first data set representing time-varying information on at least one pulse pressure wave within vasculature at a first body part of a subject;

obtaining, using a second sensor, a second data set representing time-varying information about motion of the subject at the first body part of a subject;

identifying, using one or more processors, a first point in the first data set, the first point representing an arrival time of the pulse pressure wave at the first body part;

identifying, using the one or more processors, a second point in the second dataset, the second point representing an earlier time at which the pulse pressure wave traverses a second body part of the subject; and

computing a pulse transit time (PTT) as a difference between the first and second points, the PTT representing a time taken by the pulse pressure wave to travel from the second body part to the first body part of the subject.

2. The method of claim 1, wherein the information about the at least one pulse pressure wave comprises photoplethysmographic (PPG) data and the information about motion of the subject comprises one or both of motioncardiogram (MoCG) data and gross motion data.

3. The method of claim 1, wherein data comprising at least one of the first data set and the second data set is acquired continuously.

4. The method of claim 3, wherein the data is acquired at a frequency of at least 16 Hz.

5. The method of claim 3, wherein the data is acquired at a frequency of between 75 Hz and 85 Hz.

6. The method of claim 3, wherein the data is acquired by a device worn by the subject.

7. The method of claim 6, wherein the device is mobile and does not reduce a mobility of the subject.

8. The method of claim 7, wherein the device processes the data.

9. The method of claim 1, wherein the first body part is an arm of the subject.

10. The method of claim 1, wherein the first body part is a wrist of the subject.

11. The method of claim 1, wherein the first sensor comprises an optical sensor and the second sensor comprises an accelerometer or a gyroscope.

12. The method of claim 1, wherein identifying the first point comprises:

computing, by the one or more processors, a cross-correlation of a template segment with each of multiple segments of the first dataset;

identifying, based on the computed cross-correlations, at least one candidate segment of the first dataset as including the first point; and

identifying, by the one or more processors, a first feature within the identified candidate segment as the first point.

13. The method of claim 1, wherein identifying the second point comprises:

determining a reference point in the second data set, the reference point corresponding to substantially the same point in time as the first point in the first data set;

identifying one or more target features within a predetermined time range relative to the reference point; and

selecting a time point corresponding to one of the target features as the second point.

14. The method of claim 13, wherein the target features comprises at least one of a peak and a valley.

15. The method of claim 1, further comprising computing a blood pressure of the subject as a function of the PTT.

16. The method of claim 15, wherein the blood pressure includes a systolic pressure and a diastolic pressure.

17. The method of claim 16, wherein a systolic pressure is calculated as a linear function of the diastolic pressure.

18. The method of claim 13, in which the pre-determined time range is associated with the systole portion of the subject'"'"'s heartbeat.

19. The method of claim 1, further comprising accepting user-input for initiating computation of the PTT.

20. The method of claim 1, further comprising computing arterial stiffness as a function of the PTT.

21. A biofeedback device configured to be worn by a subject, the device comprising:

a first sensor configured to obtain a first data set representing time-varying information on at least one pulse pressure wave within vasculature at a first body part of a subject;

a second sensor configured to obtain a second data set representing time-varying information about motion of the subject at the first body part of a subject;

memory; and

one or more processors configured to;

receive the first and second data sets,

identify a first point in the first data set, the first point representing an arrival time of the pulse pressure wave at the first body part,

identify a second point in the second dataset, the second point representing an earlier time at which the pulse pressure wave traverses a second body part of the subject, and

compute a pulse transit time (PTT) as a difference between the first and second points, the PTT representing a time taken by the pulse pressure wave to travel from the second body part to the first body part of the subject.

22. The device of claim 21, wherein the information about the at least one pulse pressure wave comprises photoplethysmographic (PPG) data and the information about motion of the subject comprises one or both of motioncardiogram (MoCG) data and gross motion data.

23. The device of claim 21, wherein data comprising at least one of the first data set and the second data set is acquired continuously.

24. The device of claim 23, wherein the data is acquired at a frequency of at least 16 Hz.

25. The device of claim 23, wherein the data is acquired at a frequency of between 75 Hz and 85 Hz.

26. The device of claim 21, further comprising a mechanism that allows the device to be worn by the subject.

27. The device of claim 21, wherein the mechanism does not reduce a mobility of the subject.

28. The device of claim 21, wherein the first body part is an arm of the subject.

29. The device of claim 21, wherein the first body part is a wrist of the subject.

30. The device of claim 21, wherein the first sensor comprises an optical sensor and the second sensor comprises an accelerometer or a gyroscope.

31. The device of claim 21, wherein identifying the first point comprises:

computing, by the one or more processors, a cross-correlation of a template segment with each of multiple segments of the first dataset;

identifying, based on the computed cross-correlations, at least one candidate segment of the first dataset as including the first point; and

identifying, by the one or more processors, a first feature within the identified candidate segment as the first point.

32. The device of claim 21, wherein identifying the second point comprises:

determining a reference point in the second data set, the reference point corresponding to substantially the same point in time as the first point in the first data set;

identifying one or more target features within a predetermined time range relative to the reference point; and

selecting a time point corresponding to one of the target features as the second point.

33. The device of claim 32, wherein the target features comprises at least one of a peak and a valley.

34. The device of claim 21, wherein the one or more processors are further configured to compute a blood pressure of the subject as a function of the PTT.

35. The device of claim 34, wherein the blood pressure includes a systolic pressure and a diastolic pressure.

36. The device of claim 35, wherein a systolic pressure is calculated as a function of the diastolic pressure.

37. The device of claim 32, in which the pre-determined time range is associated with the systole portion of the subject'"'"'s heartbeat.

38. The device of claim 21, further comprising an input mechanism configured to accept user-input for initiating computation of the PTT.

39. The device of claim 21, wherein the one or more processors are further configured to compute arterial stiffness as a function of the PTT.

40. One or more machine-readable storage devices storing instructions that are executable by one or more processing devices to perform operations comprising:

obtaining a first data set representing time-varying information on at least one pulse pressure wave within vasculature at a first body part of a subject;

obtaining a second data set representing time-varying information about motion of the subject at the first body part of a subject;

identifying a first point in the first data set, the first point representing an arrival time of the pulse pressure wave at the first body part;

identifying a second point in the second dataset, the second point representing an earlier time at which the pulse pressure wave traverses a second body part of the subject; and

computing a pulse transit time (PTT) as a difference between the first and second points, the PTT representing a time taken by the pulse pressure wave to travel from the second body part to the first body part of the subject.