Wearable physiological monitoring and notification system based on real-time heart rate variability analysis

• US 10,022,057 B1
• Filed: 05/11/2017
• Issued: 07/17/2018
• Est. Priority Date: 06/19/2015
• Status: Active Grant

• Current Assignee: Michael Blake, Rodney Kugizaki
• Original Assignee: Michael Blake, Rodney Kugizaki
• Inventors: Blake, Michael, Kugizaki, Rodney
• Primary Examiner(s): Schaetzle, Kennedy
• Application Number:
  US15/592,566
• Time in Patent Office: 432 Days
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation

Abstract

Methods, devices, and systems for monitoring heart rate variability (HRV) are presented. The HRV monitoring systems and devices are adapted to give immediate feedback to the subject concerning their current condition and any pertinent changes in their condition. The HRV monitoring systems and devices detect, analyze, and assess HRV against a pre-determined application or user need. They also have the ability to provide real time notifications based on the system'"'"'s assessment of a user'"'"'s heart HRV and changes in the HRV.

Claims

1. A method for real-time detection, analysis, and application of heart rate variability (HRV) using a wearable device, the method comprising:

(a) detecting a plurality of consecutive heartbeat related waves using one or more electrodes integrated within the wearable device (200);

(b) detecting a specific wave characteristic of each heartbeat related wave, of the plurality of consecutive heartbeat related waves (201);

(c) triggering an event when each specific wave characteristic crosses a trigger point (202), wherein the event is based on one or more inputs unrelated to the plurality of consecutive heartbeat related waves,

(d) recording a time value for each event triggered (203);

(e) determining and recording an interval for each time value to generate a temporal interval array (204);

(f) analyzing the plurality of consecutive heart beat related waves, each time value, and each interval for suitability of use for HRV related applications, said analysis comprises;

(i) detecting a presence of one or more ectopic beats from the plurality of consecutive heartbeat related waves (205);

(ii) correcting each time value and interval associated with each ectopic beat (206);

(iii) determining the suitability for application of each time value and each interval for a set of predetermined or dynamically set conditions (207); and

(iv) determining one or more HRV measurements based on data in the temporal interval array (208); and

wherein the one or more HRV measurements are combined with one or more additional predetermined or dynamically set input to determine when an alert is sent to one or more pre-determined parties via a real-time notification (209).

2. The method of claim 1, wherein the one or more HRV measurements are time dependent HRV values.

3. The method of claim 1, wherein the event comprises a time capture, a real-time clock value capture, or an independent timer value capture.

4. The method of claim 2, wherein the temporal interval array is a temporal R-R interval array.

5. The method of claim 4, wherein corrected R-R intervals and time values are generated by correcting each R-R interval and time value associated with each ectopic beat, said correction comprises:

(a) comparing an R-R interval and time value of an ectopic beat to a predetermined value,

(b) replacing the R-R interval and time value with a calculated R-R interval value and a calculated time value if the R-R interval and the time value exceed the predetermined value, and

(c) repeating (a) and (b) for each R-R interval and time value associated with an ectopic beat.

6. The method of claim 5, wherein the one or more HRV measurements are determined based on corrected R-R intervals and corrected time values.

7. The method of claim 5, wherein the method of generating the calculated R-R interval value and the calculated time value comprises:

standard deviation of normal to normal R-R intervals (“

SDNN”

), root mean square of successive N-N interval differences (“

RMSSD”

), standard deviation of successive differences (“

SDSD”

), standard deviation of averages of N-N intervals (“

SDANN”

), triangular interpolation of N-N interval histogram (“

TINN”

), NN count divided by total NN interval (“

pNNx”

), Hilbert Transform, approximate entropy (“

ApEn”

), sample entropy (“

SampEn”

), fuzzy entropy (“

FuzzEn”

), fuzzy measure entropy (“

FuzzMEn”

), or spectral HRV measurements including;

power in very low frequency (“

VLF”

), power in low frequency (“

LF”

), power in high frequency (“

HF”

), a ratio of LF to HF, standard deviation of Poincare plot perpendicular to a line of identity (“

SD1”

), standard deviation of axis of Poincare plot parallel to the line of identity (“

SD2”

), or a ratio of SD1 to SD2.

8. The method of claim 1, wherein the one or more predetermined or dynamically set inputs comprise:

respiration rate, blood pressure value, body temperature, level of physical motion, heart rate, a pre-determined condition marker, a pre-existing condition specification, or a level of electrodermal activity.

9. The method of claim 8, wherein the pre-determined condition marker comprises a blood sugar level.

10. The method of claim 8, wherein the pre-existing condition specification comprises:

sleep apnea, physical exertion, tendency for epileptic seizure, diabetes, or stress.

11. The method of claim 1, wherein the real-time notification comprises:

a haptic indicator, an auditory indicator, or a visual indicator.

12. A wearable device (100) for real-time detection, analysis, and application of heart rate variability (HRV), the device comprising:

(a) a chest strap (120) or wearable article integrated with one or more strap electrodes (307) to detect a plurality of consecutive QRS waves; and

(b) a battery-powered, self-contained processing circuit (110) comprising;

(i) a microprocessor (301), operatively coupled to the one or more strap electrodes (307), configured to receive one or more additional inputs unrelated to the plurality of consecutive QRS waves detected by the one or more strap electrodes (307);

(ii) an electrocardiogram (“

ECG”

) analog front end (“

AFE”

) circuit (306), coupled between the microprocessor (301) and the one or more strap electrodes (307), providing a signal gain control to an output of the one or more strap electrodes (307) so that the output has a desired amplitude before transmission to the microprocessor (301);

(iii) a non-volatile memory (308), operatively coupled to the microprocessor (301), storing computer-readable instructions which, when executed by the microprocessor (301), cause the microprocessor (301) to perform operations comprising;

(A) detecting a rising edge, a peak, or a declining edge of an R- wave from each QRS wave;

(B) triggering an event when the rising edge, the declining edge, or the peak of each R-wave crosses a trigger point, wherein the trigger point is a predetermined value or a dynamically adjusted value;

(C) recording a time value for each event triggered;

(D) determining and recording an R-R interval for each time value to generate a temporal R-R interval array, which is stored in the non-volatile memory (308);

(E) analyzing the plurality of consecutive heart beat related waves, each time value, and each interval for suitability of use for HRV related applications, said analysis comprises;

(I) detecting a presence of one or more ectopic beats from the plurality of consecutive heartbeat related waves;

(II) correcting each time value and interval associated with each ectopic beat;

(III) determining the suitability for application of each time value and each interval for a set of predetermined or dynamically set conditions; and

(IV) determining one or more HRV measurements based on data in the temporal R-R interval array; and

wherein the one or more HRV measurements are combined with one or more additional predetermined or dynamically set input to determine when an alert is sent to one or more pre-determined parties via a real-time notification.

13. The device (101) of claim 12, wherein the event comprises a time capture, a real-time clock value capture, or an independent timer value capture.

14. The device (101) of claim 12, wherein corrected R-R intervals and time values are generated by correcting each R-R interval and time value associated with each ectopic beat, said correction comprises:

(a) comparing an R-R interval and time value of an ectopic beat to a predetermined value,

(b) replacing the R-R interval and time value with a calculated R-R interval value and a calculated time value if the R-R interval and the time value exceed the predetermined value, and

(c) repeating (a) and (b) for each R-R interval and time value associated with an ectopic beat.

15. The device (101) of claim 14, wherein the one or more HRV measurements are determined based on corrected R-R intervals and time values.

16. The device (101) of claim 14, wherein the method of generating the calculated R- R interval value and the corrected time value comprises:

standard deviation of normal to normal R-R intervals (“

SDNN”

), root mean square of successive N-N interval differences (“

RMSSD”

), standard deviation of successive differences (“

SDSD”

), standard deviation of averages of N-N intervals (“

SDANN”

), triangular interpolation of N-N interval histogram (“

TINN”

), NN count divided by total NN interval (“

pNNx”

), Hilbert Transform, approximate entropy (“

ApEn”

), sample entropy (“

SampEn”

), fuzzy entropy (“

FuzzEn”

), fuzzy measure entropy (“

FuzzMEn”

), or spectral HRV measurements including;

power in very low frequency (“

VLF”

), power in low frequency (“

LF”

), power in high frequency (“

HF”

), a ratio of LF to HF, standard deviation of Poincare plot perpendicular to a line of identity (“

SD1”

), standard deviation of axis of Poincare plot parallel to the line of identity (“

SD2”

), or a ratio of SD1 to SD2.

17. The device (101) of claim 12, wherein the one or more predetermined or dynamically set inputs comprise:

respiration rate, blood pressure value, body temperature, level of physical motion, heart rate, a pre-determined condition marker, a pre-existing condition specification, or a level of electrodermal activity.

18. The device (101) of claim 17, wherein the pre-determined condition marker comprises a blood sugar level.

19. The device (101) of claim 17, wherein the pre-existing condition specification comprises:

sleep apnea, physical exertion, tendency for epileptic seizure, diabetes, or stress.

20. The device (101) of claim 12, wherein the real-time notification (303) comprises a haptic indicator, an auditory indicator, or a visual indicator disposed on the device (101).