IP Library Granted Patent US 12,599,335
Granted Patent B2
US 12,599,335 · App. 18/307,756 · Granted Apr 14, 2026

Detection and monitoring of sleep apnea conditions

Inventors: Yong K. Cho (Excelsior, MN); Eduardo N. Warman (Maple Grove, MN); Gautham Rajagopal (Minneapolis, MN)
Assignee: Medtronic, Inc.
A61B5/4818A61B5/02405A61B5/0245A61B5/349G16H40/67G16H50/30A61B5/29A61B5/318A61B5/686
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Quick Facts
Patent No.
US 12,599,335
App. No.
18/307,756
Granted
Apr 14, 2026
Kind
B2
Abstract

A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

Claims (47)

1 . A system comprising:

an implantable medical device comprising:

a set of electrodes; and

sensing circuitry configured to sense, via the set of electrodes, a cardiac signal indicating activity of a heart of a patient; and

processing circuitry configured to:

determine a heart rate cycle based on a short-term average of a heart rate and a long-term average of the heart rate, wherein the heart rate cycle defines a period of time of the activity of the heart for detecting an occurrence of a sleep apnea episode, and wherein the short-term average and the long-term average are based on the cardiac signal; and

determine whether a peak-to-valley time interval of the heart rate cycle satisfies a peak-to-valley time interval condition, wherein the peak-to-valley time interval is a time interval between a maximum short-term average of the heart rate during the heart rate cycle and a minimum short-term average of the heart rate during the heart rate cycle; and

wherein the implantable medical device or another medical device is configured to deliver an electrical or airway pressure therapy based at least in part on the processing circuitry determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition.

2 . The system of claim 1 , wherein the processing circuitry is configured to determine that the peak-to-valley time interval satisfies the peak-to-valley time interval condition in response to the peak-to-valley time interval being greater than a lower peak-to-valley time threshold and less than an upper peak-to-valley time threshold.

3 . The system of claim 1 , wherein the processing circuitry is further configured to:

determine whether an activity count of the heart rate cycle satisfies an activity count condition, wherein the activity count indicates a number of time intervals during the heart rate cycle in which an amount of movement of the patient is greater than a minimum movement threshold; and

based at least in part on determining that the activity count satisfies the activity count condition and determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition, output an indication that the sleep apnea episode occurred during the heart rate cycle.

4 . The system of claim 3 , wherein the processing circuitry is configured to determine that the activity count satisfies the activity count condition in response to the activity count being less than an activity count threshold.

5 . The system of claim 4 , wherein the activity count threshold is equal to 8.

6 . The system of claim 1 , wherein the processing circuitry is further configured to:

determine whether a peak-to-valley heart rate variation value of the heart rate cycle satisfies a peak-to-valley heart rate variation condition, wherein the peak-to-valley heart rate variation value indicates a difference between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle; and

based at least in part on determining that the peak-to-valley heart rate variation value satisfies the peak-to-valley heart rate variation condition and determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition, output an indication that the sleep apnea episode occurred during the heart rate cycle.

7 . The system of claim 6 , wherein the processing circuitry is configured to determine that the peak-to-valley heart rate variation value satisfies the peak-to-valley heart rate variation condition in response to the peak-to-valley heart rate variation value being between a lower peak-to-valley heart rate variation threshold and an upper peak-to-valley heart rate variation threshold.

8 . The system of claim 7 , wherein at least one of the lower peak-to-valley heart rate variation threshold is equal to 6 beats per minute, or the upper peak-to-valley heart rate variation threshold is equal to 50 beats per minute.

9 . The system of claim 1 , wherein the processing circuitry is further configured to:

determine whether a cycle length of the heart rate cycle satisfies a cycle length condition, wherein the cycle length indicates a length of the heart rate cycle; and

based at least in part on determining that the cycle length satisfies the cycle length condition and determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition, output an indication that the sleep apnea episode occurred during the heart rate cycle.

10 . The system of claim 9 , wherein the processing circuitry is configured to determine that the cycle length satisfies the cycle length condition in response to the cycle length being greater than a lower cycle length threshold and less than an upper cycle length threshold.

11 . The system of claim 1 , wherein the heart rate cycle is a current heart rate cycle, and wherein the processing circuitry is further configured to:

determine whether at least one additional heart rate cycle that occurs within a predetermined temporal distance of the current heart rate cycle satisfies at least one condition of a plurality of conditions, wherein each condition of the plurality of conditions is associated with the occurrence of the sleep apnea episode; and

based at least in part on determining that the at least one additional heart rate cycle satisfies the at least one condition of the plurality of conditions, output an indication that the sleep apnea episode occurred during the current heart rate cycle.

12 . The system of claim 11 , wherein the plurality of conditions comprises at least one of the peak-to-valley time interval condition, an activity count condition, a peak-to-valley heart rate variation condition, or a cycle length condition.

13 . The system of claim 11 , wherein the predetermined temporal distance is 240 seconds.

14 . The system of claim 1 , wherein the implantable medical device comprises the processing circuitry, and wherein the system further comprises an external device configured to receive an indication that the sleep apnea episode occurred during the heart rate cycle.

15 . The system of claim 1 , wherein, to determine the heart rate cycle, the processing circuitry is configured to:

determine a start of the heart rate cycle based on a first time the short-term average of the heart rate changes from being less than the long-term average of the heart rate to being greater than the long-term average of the heart rate; and

determine an end of the heart rate cycle based on a second time the short-term average of the heart rate changes from being less than the long-term average of the heart rate to being greater than the long-term average of the heart rate of the patient.

16 . An implantable medical device comprising:

a set of electrodes;

sensing circuitry configured to sense, via the set of electrodes, a cardiac signal indicating activity of a heart of a patient; and

processing circuitry configured to:

determine a heart rate cycle based on a short-term average of a heart rate and a long-term average of the heart rate, wherein the heart rate cycle defines a period of time of the activity of the heart for detecting an occurrence of a sleep apnea episode, and wherein the short-term average and the long-term average are based on the cardiac signal;

determine whether a peak-to-valley time interval of the heart rate cycle satisfies a peak-to-valley time interval condition, wherein the peak-to-valley time interval is a time interval between a maximum short-term average of the heart rate during the heart rate cycle and a minimum short-term average of the heart rate during the heart rate cycle; and

based at least in part on determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition, output an indication that the sleep apnea episode occurred during the heart rate cycle.

17 . The implantable medical device of claim 16 , wherein the processing circuitry is configured to determine that the peak-to-valley time interval satisfies the peak-to-valley time interval condition in response to the peak-to-valley time interval being greater than a lower peak-to-valley time threshold and less than an upper peak-to-valley time threshold.

18 . The implantable medical device of claim 17 , wherein the processing circuitry is further configured to:

determine whether an activity count of the heart rate cycle satisfies an activity count condition, wherein the activity count indicates a number of time intervals during the heart rate cycle in which an amount of movement of the patient is greater than a minimum movement threshold; and

based at least in part on determining that the activity count satisfies the activity count condition and determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition, output the indication that the sleep apnea episode occurred during the heart rate cycle.

19 . A method comprising:

determining, by processing circuitry, a heart rate cycle based on a short-term average of a heart rate and a long-term average of the heart rate, wherein the heart rate cycle defines a period of time of activity of a heart of a patient for detecting an occurrence of a sleep apnea episode, and wherein the short-term average and the long-term average are based on a cardiac signal that indicates the activity of the heart of the patient;

determining, by the processing circuitry, whether a peak-to-valley time interval of the heart rate cycle satisfies a peak-to-valley time interval condition, wherein the peak-to-valley time interval is a time interval between a maximum short-term average of the heart rate during the heart rate cycle and a minimum short-term average of the heart rate during the heart rate cycle; and

delivering, by a medical device, an electrical or airway pressure therapy based at least in part on the processing circuitry determining that the peak-to-valley time interval satisfies the peak-to-valley time interval condition.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 26, 2023
From: CHO, YONG K.; WARMAN, EDUARDO N.; RAJAGOPAL, GAUTHAM
To: MEDTRONIC, INC.
Reel/Frame 063454/0532 →
Continuity (2)
Continuation 17116482 · Dec 9, 2020
Related Publication 20230363701A1 · Nov 16, 2023
References Cited (52)
US 6881192B1 · Park · 2005 [cited by applicant]
US 7438686B2 · Cho et al. · 2008 [cited by applicant]
US 7460899B2 · Almen · 2008 [cited by applicant]
US 8083682B2 · Dalal et al. · 2011 [cited by applicant]
US 8475388B2 · Ni et al. · 2013 [cited by applicant]
US 8630709B2 · Libbus et al. · 2014 [cited by applicant]
US 8721560B2 · Koh · 2014 [cited by applicant]
US 8821418B2 · Meger et al. · 2014 [cited by applicant]
US 8998830B2 · Halperin et al. · 2015 [cited by applicant]
US 9011341B2 · Jensen et al. · 2015 [cited by applicant]
US 9757560B2 · Papay · 2017 [cited by applicant]
US 10028699B2 · Libbus et al. · 2018 [cited by applicant]
US 10029098B2 · Papay · 2018 [cited by applicant]
US 10065038B2 · Papay · 2018 [cited by applicant]
US 10265015B2 · Bardy et al. · 2019 [cited by applicant]
US 10321871B2 · Bandyopadhyay et al. · 2019 [cited by applicant]
US 10595813B2 · Song et al. · 2020 [cited by applicant]
US 10744339B2 · Makansi · 2020 [cited by applicant]
US 20030055348A1 · Chazal et al. · 2003 [cited by applicant]
US 20040134496A1 · Cho et al. · 2004 [cited by applicant]
US 20050119711A1 · Cho · 2005 [cited by examiner]
US 20050267362A1 · Mietus et al. · 2005 [cited by applicant]
US 20060241708A1 · Boute · 2006 [cited by applicant]
US 20070032733A1 · Burton · 2007 [cited by applicant]
US 20070239055A1 · Sowelam et al. · 2007 [cited by applicant]
US 20100262032A1 · Freeberg · 2010 [cited by applicant]
US 20140221850A1 · Farringdon et al. · 2014 [cited by applicant]
US 20140276928A1 · Vanderpool et al. · 2014 [cited by applicant]
US 20150112606A1 · He et al. · 2015 [cited by applicant]
US 20160066796A1 · Shinozaki et al. · 2016 [cited by applicant]
US 20160310031A1 · Sarkar · 2016 [cited by applicant]
US 20170354365A1 · Zhou · 2017 [cited by applicant]
US 20180168502A1 · Cho et al. · 2018 [cited by applicant]
US 20190059816A1 · Katra et al. · 2019 [cited by applicant]
US 20200107775A1 · de Chazal et al. · 2020 [cited by applicant]
US 20200147376A1 · Dieken et al. · 2020 [cited by applicant]
US 20200269044A1 · Papay · 2020 [cited by applicant]
US 20200338358A1 · Makansi · 2020 [cited by applicant]
US 20200346017A1 · Caparso et al. · 2020 [cited by applicant]
US 20210369191A1 · Gill et al. · 2021 [cited by applicant]
US 20220175310A1 · Cho et al. · 2022 [cited by applicant]
US 20250064391A1 · Rajagopal et al. · 2025 [cited by applicant]
CN 204765621U · 2015 [cited by applicant]
EP 3071288B1 · 2018 [cited by applicant]
WO 2020263451A1 · 2020 [cited by applicant]
WO 2020263568A1 · 2020 [cited by applicant]
Hayano et al., “Screening for Obstructive Sleep Apnea by Cyclic Variation of Heart Rate,” Circulation: Arrhythmia and Electrophysiology, vol. 4, No. 1, Feb. 2011, 9 pp. [cited by applicant]
International Search Report and Written Opinion of International Application No. PCT/US2021/062105, dated Mar. 30, 2022, 15 pp. [cited by applicant]
Nakayama et al., “Obstructive Sleep Apnea Screening by Heart Rate Variability-Based Apnea/Normal Respiration Discriminant Model,” Physiological Measurement, vol. 40, Dec. 20, 2019, 12 pp. [cited by applicant]
Penzel et al., “Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography,” Frontiers in Physiology, vol. 7, Article 460, Oct. 25, 2016, 15 pp. [cited by applicant]
Prosecution History from U.S. Appl. No. 17/116,482, dated Jun. 22, 2022 through Feb. 1, 2023, 64 pp. [cited by applicant]
Tong et al., “Detection of Sleep Apnea-Hypopnea Syndrome with ECG Derived Respiration in Chinese Population,” International Journal of Clinical and Experimental Medicine, vol. 7, No. 5, Jul. 2014, pp. 1269-1275. [cited by applicant]
Cited By (1)
US 12,710,803