Generative Model-Driven Resource-Efficient Monitoring in Body Sensor Networks

• US 20130317377A1
• Filed: 05/23/2013
• Published: 11/28/2013
• Est. Priority Date: 05/23/2012
• Status: Abandoned Application

• Current Assignee: Arizona Board of Regents (University of Arizona), University of Washington
• Original Assignee: Arizona Board of Regents (University of Arizona), University of Washington
• Inventors: Nabar, Sidharth, Poovendran, Radha, Gupta, Sandeep, Banerjee, Ayan
• Application Number:
  US13/901,442
• Publication Number:
  US 20130317377A1
• Time in Patent Office: Days
• US Class Current: 600/515
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/35 by template matching

Abstract

Body sensor networks (BSNs) and methods for monitoring an electrocardiogram using such BSNs include a base station that generates an ECG model and an output ECG signal for displaying on a display device, and a sensor platform in electrical communication with the base station. The sensor platform may be configured to receive a sensed ECG signal from one or more sensors, receive an instance of the ECG model, and produce a model ECG signal from the instance. The sensor platform compares the sensed ECG signal to the model ECG signal and, if a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, transmits deviation data describing the deviation to the base station module. The sensor platform module does not transmit any data to the base station if there is no such deviation.

Claims

1. A method for monitoring an electrocardiogram (ECG) of a patient, the method comprising:

receiving a sensed ECG signal from one or more sensors configured to collect the sensed ECG signal from the patient;

comparing the sensed ECG signal to a model ECG signal; and

if a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, transmitting deviation data describing the deviation to a base station.

2. The method of claim 1, further comprising transmitting no data other than the deviation data to the base station.

3. The method of claim 1, further comprising periodically transmitting an acknowledgment signal to the base state, and transmitting no data other than the deviation data and the acknowledgment signal to the base station.

4. The method of claim 1, wherein comparing the sensed ECG signal to the model ECG signal comprises one or more of:

performing feature value calculations; and

performing direct comparisons of the sensed ECG signal to the model ECG signal.

5. The method of claim 1, further comprising generating an output ECG signal, wherein the output ECG signal comprises the model ECG signal when no deviation data is transmitted, and wherein the output ECG signal comprises a modification to the model ECG signal when deviation data is transmitted, the modification being generated based on the deviation data.

6. The method of claim 1, wherein the deviation data comprises one or more feature updates.

7. The method of claim 6, further comprising updating the model ECG signal based on the one or more feature updates.

8. The method of claim 1, wherein the deviation data is raw data comprising a portion of the sensed ECG signal.

9. A method for monitoring an electrocardiogram (ECG) of a patient, the method comprising

receiving at a sensor platform a sensed ECG signal from one or more sensors configured to collect the sensed ECG signal from the patient;

comparing, at the sensor platform, the sensed ECG signal to a model ECG signal;

if a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, transmitting, from the sensor platform, deviation data describing the deviation to a base station; and

generating, at the base station, an output ECG signal to be displayed on a display device, wherein the output ECG signal comprises the model ECG signal and, when deviation data is received, further comprises a modification to the model ECG signal.

10. The method of claim 9, wherein comparing the sensed ECG signal to a model ECG signal comprises performing calculations of one or more feature values of the sensed ECG signal, comparing the one or more feature values to one or more corresponding model parameter values of the model ECG signal, and generating the deviation data comprising any of the one or more feature values that deviates from the corresponding model parameter values beyond the threshold.

11. The method of claim 10, further comprising updating an ECG model, from which the model ECG signal is derived, based on the deviation data.

12. The method of claim 9, wherein comparing the sensed ECG signal to a model ECG signal comprises:

obtaining a set of consecutive beats from the sensed ECG signal;

calculating a representative beat for the sensed ECG signal, comprising the average of the set of consecutive beats;

directly comparing the representative beat for the sensed ECG signal to a representative beat for the model ECG signal; and

generating the deviation data as raw data comprising either or both of the set of consecutive beats and the representative beat for the sensed ECG signal.

13. The method of claim 12, wherein the modification to the model ECG signal comprises an abnormal ECG signal generated using the deviation data.

14. The method of claim 9 further comprising periodically transmitting, from the sensor platform to the base station module, an acknowledgment signal, wherein the sensor platform does not transmit any data to the base station module other than the deviation data and the acknowledgement signal.

15. The method of claim 9, further comprising training, at the base station, an ECG model from which the model ECG signal is derived, the training comprising:

receiving a training ECG from the patient;

calculating one or more interbeat parameters from the training ECG;

calculating one or more morphology parameters from the training ECG; and

generating the ECG model using the interbeat parameters and the morphology parameters as inputs.

16. The method of claim 15, further comprising distributing the ECG model from the base station to the sensor platform.

17. A body sensor network for monitoring an electrocardiogram of a patient, the body sensor network comprising:

a base station comprising a base station module configured to generate an ECG model and to generate an output ECG signal for displaying on a display device; and

a sensor platform in electrical communication with the base station, the sensor platform comprising a sensor platform module configured to;

receive a sensed ECG signal from one or more sensors attached to the patient and collecting the patient'"'"'s ECG embodied in the sensed ECG signal;

receive an instance of the ECG model and produce a model ECG signal from the instance;

compare the sensed ECG signal to the model ECG signal; and

if a deviation of the sensed ECG signal from the model ECG signal exceeds a threshold, transmit deviation data describing the deviation to the base station module;

wherein the sensor platform module does not transmit the sensed ECG signal if there is no deviation of the sensed ECG signal from the model ECG signal exceeding the threshold.

18. The body sensor network of claim 17, wherein the sensor platform module compares the sensed ECG signal to a model ECG signal by:

performing calculations of one or more feature values of the sensed ECG signal;

comparing the one or more feature values to one or more corresponding model parameter values of the model ECG signal; and

generating the deviation data comprising any of the one or more feature values that deviates from the corresponding model parameter values beyond the threshold;

and wherein the base station module is configured to update the ECG model based on the deviation data.

19. The body sensor network of claim 17, wherein the sensor platform module compares the sensed ECG signal to a model ECG signal by:

obtaining a set of consecutive beats from the sensed ECG signal;

calculating a representative beat for the sensed ECG signal, comprising the average of the set of consecutive beats;

directly comparing the representative beat for the sensed ECG signal to a representative beat for the model ECG signal; and

generating the deviation data as raw data comprising either or both of the set of consecutive beats and the representative beat for the sensed ECG signal;

and wherein the output ECG signal comprises an abnormal ECG signal that is based on the deviation data.