System and methods of determining etiology of undiagnosed symptomatic events

• US 10,368,808 B2
• Filed: 11/02/2016
• Issued: 08/06/2019
• Est. Priority Date: 11/02/2016
• Status: Active Grant

• Current Assignee: Medtronic Monitoring, Inc. (Medtronic Plc)
• Original Assignee: Medtronic Monitoring, Inc. (Medtronic Plc)
• Inventors: Lee, Brian B., Musley, ShaileshKumar V., Stadler, Robert W., Shrivastav, Maneesh, Schulhauser, Randal, Vilendrer, Stacie
• Primary Examiner(s): Weare, Meredith
• Application Number:
  US15/341,465
• Publication Number:
  US 20180116598A1
• Time in Patent Office: 1,007 Days
• CPC Class Codes: A61B 2560/0412 Low-profile patch shaped housings
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/022 by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
• CPC Class Codes: A61B 5/1032 Determining colour for diagnostic purposes measuring colour in general G01J3/46
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/259 using conductive adhesive means, e.g. gels
• CPC Class Codes: A61B 5/28 for electrocardiography [ECG]
• CPC Class Codes: A61B 5/282 Holders for multiple electrodes for introduction into the body A61B5/287
• CPC Class Codes: A61B 5/6832 using adhesives A61B5/259 takes 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 5/7282 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Embodiments describe a method of determining etiology of undiagnosed events comprising monitoring electrocardiogram signals and blood pressure of a patient via a medical device, capturing one or more of an ECG segment and a BP reading in response to a triggering event, classifying one or more of the ECG segment and BP reading as normal or abnormal, and determining etiology of undiagnosed symptomatic events based on the classification. Embodiments further describe a medical device comprising sensors for monitoring ECG signals and BP of a patient, circuitry for capturing one or more of ECG segments and BP readings of a patient in response to a triggering event, and a processor for communicating one of more of captured ECG segments and captured BP readings to a remote monitoring center directly or indirectly where the captured ECG segments and captured BP readings are classified as normal or abnormal.

Claims

1. A method of determining etiology of undiagnosed events, the method comprising:

monitoring electrocardiogram (ECG) signals of a patient via an adherent device;

capturing an ECG segment in response to a triggering event, wherein the triggering event includes a patient triggered event and/or an automatically triggered event in which the monitored ECG signal deviates from baseline data;

prompting the patient via the adherent device to wear a satellite blood pressure cuff in response to the triggering event, wherein the satellite blood pressure cuff captures blood pressure (BP) readings and communicates the captured BP readings to the adherent device;

communicating the captured ECG segment and captured BP readings from the adherent device to a remote monitoring center;

classifying, at the remote monitoring center, the ECG segment and BP reading as normal or abnormal; and

determining etiology of undiagnosed symptomatic events based on the classification of the ECG segment and/or BP reading.

2. The method of claim 1, wherein the ECG segment and/or BP reading captured includes one or more of prospectively captured ECG segments, prospectively captured BP readings, recently captured ECG segments, and recently captured BP readings.

3. The method of claim 1, wherein the baseline data includes one or more of patient-specific ECG data and population-based ECG data.

4. The method of claim 1, wherein the ECG segment and/or BP reading is classified as normal or abnormal based on reference data.

5. The method of claim 1, wherein an ECG segment is classified as normal or abnormal based on a previously captured ECG segment.

6. The method of claim 1, wherein an ECG segment is classified as abnormal if an abnormal heart rhythm is detected.

7. The method of claim 1, wherein an ECG segment is classified as abnormal if a significant ECG change is detected.

8. The method of claim 1, wherein a BP reading is classified as normal or abnormal based on a previously captured BP reading.

9. The method of claim 1, wherein a BP reading is classified as abnormal if a significant BP change is detected.

10. A medical system, the medical system comprising:

an adherent device configured to be adhered to a patient, the adherent device having one or more sensors for monitoring ECG signals of the patient and circuitry for capturing ECG segments in response to a triggering event;

a satellite blood pressure monitor configured to be positioned on the patient'"'"'s arm for monitoring blood pressure of the patient and capturing BP readings, the satellite blood pressure monitor having communication circuitry for communicating the captured BP readings to the adherent device; and

a processor located on the adherent device for communicating one of more of captured ECG segments and captured BP readings to remote monitoring center directly or indirectly where the captured ECG segments and captured BP readings are classified as normal or abnormal;

wherein in response to an automatically triggered ECG event or a patient triggered event, the adherent device prompts the patient to wear the satellite blood pressure monitor to capture BP readings.

11. The medical system of claim 10, wherein the automatically triggered events are based on the monitored ECG signal deviating from baseline data.

12. The medical system of claim 11, wherein the baseline data includes one or more of patient-specific ECG data and population-based ECG data.

13. The medical system of claim 11, wherein the processor on the adherent device compares the monitored ECG signal to the baseline data to identify automatically triggered events.

14. The medical system of claim 10, further comprising sensors for monitoring one or more of glucose levels, pulse pressure, posture, and epidermal pigment.

15. The medical system of claim 10, wherein an ECG segment is classified as abnormal if an abnormal heart rhythm and/or significant ECG change is detected.

16. The medical system of claim 10, wherein a BP reading is classified as abnormal if a significant BP change is detected.

17. The medical system of claim 10, wherein the adherent patch is worn continuously and the satellite blood pressure cuff is worn intermittently.

18. The medical system of claim 10, wherein the remote monitoring center determines an etiology of an syncopal episode based on the classification of the captured ECG segments and captured BP readings as normal or abnormal.

19. The medical system of claim 18, wherein the remote monitoring center a syncopal episode is classified as vasovagal syncope if the captured ECG segment is classified as normal and the captured BP reading is classified as abnormal.

20. The medical system of claim 18, wherein the remote monitoring center a syncopal episode is classified as cardiogenic syncope if the captured ECG segment is classified as abnormal and the captured BP reading is classified as abnormal.