PREDICTING MEDICAL EVENTS

• US 20150112156A1
• 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, Azim, Shahid, Biijjani, Richard Robehr
• Application Number:
  US14/521,823
• Publication Number:
  US 20150112156A1
• Time in Patent Office: Days
• US Class Current: 600/301
• 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 processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject. The method also includes predicting a medical event of the subject based on the processed data.

Claims

1. A method comprising:

processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject; and

predicting a medical event of the subject based on the processed data.

2. The method of claim 1, further comprising processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject.

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

4. The method of claim 1, wherein the data is acquired continuously.

5. The method of claim 1, wherein the data is acquired at a frequency of between 75 Hz and 85 Hz.

6. The method of claim 1, wherein the data is acquired at a single location of the subject.

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

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

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

10. The method of claim 6, wherein the single location is an arm of the subject.

11. The method of claim 10, wherein the single location is a wrist of the subject.

12. The method of claim 1, further comprising alerting a caregiver when a medical event of the subject is predicted.

13. The method of claim 1, wherein processing the data comprises determining one or more of heart rate, heart rate variability, blood pressure, blood pressure variability, body temperature, skin temperature, vocal tonality, electrical skin impedance, respiratory rate, blood oxygen level, stroke volume, cardiac output, MoCG morphology, and PPG morphology of the subject.

14. The method of claim 1, wherein predicting the medical event of the subject comprises determining whether a heart rate of the subject satisfies a threshold.

15. The method of claim 14, wherein the medical event is tachycardia.

16. The method of claim 14, wherein determining the heart rate of the subject comprises calculating a distance between two consecutive reference points in the first dataset, the distance representing a time that has elapsed between two consecutive heartbeats of the subject.

17. The method of claim 16, wherein the reference points are local maxima or local minima.

18. The method of claim 16, wherein the reference points are peaks or valleys.

19. The method of claim 1, wherein predicting the medical event of the subject comprises determining whether a heart rate variability of the subject satisfies a threshold.

20. The method of claim 19, wherein determining the heart rate variability of the subject comprises calculating distances between multiple pairs of consecutive reference points in the first dataset, each distance representing a time that has elapsed between two consecutive heartbeats of the subject.

21. The method of claim 2, wherein predicting the medical event of the subject comprises determining whether a blood pressure of the subject satisfies a threshold.

22. The method of claim 21, wherein the medical event is hypertension.

23. The method of claim 1, wherein predicting the medical event of the subject comprises determining a rate of change of a blood pressure of the subject.

24. The method of claim 23, wherein the medical event is a stroke, and a stroke is predicted if the rate of change of the blood pressure of the subject is positive and above a threshold.

25. The method of claim 23, wherein the medical event is abnormal heart function, and abnormal heart function is predicted if the rate of change of the blood pressure of the subject is negative and below a threshold.

26. The method of claim 21, further comprising:

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

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.

27. The method of claim 26, wherein the blood pressure of the subject is determined based on the PTT.

28. The method of claim 26, wherein the first body part is the location of the subject at which the data in the first data set is acquired, and the second body part is the heart of the subject.

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

a light source configured to emit light toward the skin of the subject;

an optical sensor configured to;

receive the emitted light after the emitted light reflects off of the skin of the subject; and

provide data that corresponds to a characteristic of the received light, the data representing time-varying information about at least one pulse pressure wave propagating through blood in the subject acquired by the optical sensor at a location of the subject; and

a processor configured to;

receive data from one or both of the light-emitting element and the optical sensor; and

predict a medical event of the subject based on the data.

30. The biofeedback device of claim 29, further comprising a motion sensor configured to provide data that represents time-varying information about motion of the subject acquired by the motion sensor at the location of the subject, wherein the processor is further configured to receive data from the motion sensor.

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

processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject; and

predicting a medical event of the subject based on the processed data.

32. The one or more machine-readable storage devices of claim 31, wherein the operations further comprise processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject.