Adaptive selection of digital egg filter

• US 10,123,716 B2
• Filed: 04/03/2017
• Issued: 11/13/2018
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: Vital Connect, Inc.
• Original Assignee: Vital Connect, Inc.
• Inventors: Narasimhan, Ravi, Nazari, Nersi, Ferdosi, Nima
• Primary Examiner(s): Flory, Christopher A
• Assistant Examiner(s): PHAM, MINH DUC GIA
• Application Number:
  US15/477,132
• Publication Number:
  US 20170202473A1
• Time in Patent Office: 589 Days
• Field of Search: 600521
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method and system for filtering a detected ECG signal are disclosed. In a first aspect, the method comprises filtering the detected ECG signal using a plurality of digital filters. The method includes adaptively selecting one of the plurality of digital filters to maintain a minimum signal-to-noise ratio (SNR). In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Claims

1. A method to adaptively select an ECG digital filter, comprising:

selecting a digital filter based on an activity level detected by an MEMS device of a wireless sensor device;

calculating a first quality metric of an output of the selected digital filter over a predetermined time period;

determining whether the first calculated quality metric is greater than a threshold QM_HI;

in response to determining that the first calculated quality metric is greater than a threshold QM_HI, determining whether a filter setting is at a lowest cutoff frequency setting of utilized parallel digital filters;

in response to determining that the filter setting is not at the lowest cutoff frequency, calculating a second quality metric of a second output of a previous filter setting over a second predetermined time period and determining whether the second calculated quality metric of the previous filter is greater than the threshold QM_HI;

in response to determining that the second calculated quality metric of the previous filter is greater than the threshold QM_HI, switching the selected digital filter to the previous filter setting and returning to the calculating the quality metric of the output of the selected digital filter; and

returning to the calculating the quality metric of the output of the selected digital filter step.

2. The method of claim 1, further comprising:

in response to determining that the first calculated Quality Metric is not greater than the threshold QM_HI, determining whether the first calculated Quality Metric is less than a threshold QM_LO.

3. The method of claim 2, further comprising:

in response to determining that the first calculated Quality Metric is not less than the threshold QM_LO, determining that the first calculated Quality Metric is between the threshold QM_HI and the threshold QM_LO and determining that the first calculated Quality Metric is an ECG signal with an average level of quality.

4. The method of claim 3, further comprising:

in response to determining that the first calculated Quality Metric is less than the threshold QM_LO, determining whether the filter setting is at a highest cutoff frequency setting of the utilized parallel digital filters.

5. The method of claim 4, further comprising:

in response to determining that the filter setting is at the highest cutoff frequency setting, generating a user alert based on at least one of activity level, Quality Metric, or QRS amplitude.

6. The method of claim 4, further comprising:

in response to determining that the filter setting is not at the highest cutoff frequency setting, switching to a next filter setting.

7. The method of claim 1, wherein the activity level is determined by processing body acceleration data captured in x, y, and z coordinates of the MEMS device.

8. The method of claim 7, wherein the activity level is further determined by passing the body acceleration data through a band pass filter.

9. The method of claim 8, wherein the activity level is further determined by:

calculating an absolute value of the filtered body acceleration data in three axes, and adding absolute magnitude values of the filtered body acceleration data in three axes.

10. The method of claim 9, wherein the activity level is further determined by passing the added absolute magnitude values through a low-pass filter to output the activity level.

11. A non-transitory computer-readable medium storing executable instructions that, in response to execution, cause a computer to perform operations comprising:

selecting a digital filter based on an activity level detected by a MEMS device of a wireless sensor device;

calculating a first quality metric of an output of the selected digital filter over a predetermined time period;

determining whether the first calculated quality metric is greater than a threshold QM_HI;

in response to determining that the first calculated quality metric is greater than a threshold QM_HI, determining whether a filter setting is at a lowest cutoff frequency setting of utilized parallel digital filters;

in response to determining that the filter setting is not at the lowest cutoff frequency, calculating a second quality metric of a second output of a previous filter setting over a second predetermined time period and determining whether the second calculated quality metric of the previous filter is greater than the threshold QM_HI;

in response to determining that the second calculated quality metric of the previous filter is greater than the threshold QM_HI, switching the selected digital filter to the previous filter setting and returning to the calculating the quality metric of the output of the selected digital filter; and

returning to the calculating the quality metric of the output of the selected digital filter step.

12. The computer readable medium of claim 11, storing executable instructions that, in response to further execution, cause the computer to perform further operations comprising:

in response to determining that the first calculated Quality Metric is not greater than the threshold QM_HI, determining whether the first calculated Quality Metric is less than a threshold QM_LO.

13. The computer readable medium of claim 12, storing executable instructions that, in response to further execution, cause the computer to perform further operations comprising:

in response to determining that the first calculated Quality Metric is not less than the threshold QM_LO, determining that the first calculated Quality Metric is between the threshold QM_HI and the threshold QM_LO and determining that the first calculated Quality Metric is an ECG signal with an average level of quality.

14. The computer readable medium of claim 13, storing executable instructions that, in response to further execution, cause the computer to perform further operations comprising:

in response to determining that the first calculated Quality Metric is less than the threshold QM_LO, determining whether the filter setting is at a highest cutoff frequency setting of the utilized parallel digital filters.

15. The computer readable medium of claim 14, storing executable instructions that, in response to further execution, cause the computer to perform further operations comprising:

in response to determining that the filter setting is at the highest cutoff frequency setting, generating a user alert based on at least one of activity level, Quality Metric, or QRS amplitude.

16. The computer readable medium of claim 14, storing executable instructions that, in response to further execution, cause the computer to perform further operations comprising:

in response to determining that the filter setting is not at the highest cutoff frequency setting, switching to a next filter setting.

17. The computer readable medium of claim 11, wherein the activity level is determined by processing body acceleration data captured in x, y, and z coordinates of the MEMS device.

18. The computer readable medium of claim 17, wherein the activity level is further determined by passing the body acceleration data through a band pass filter.

19. The computer readable medium of claim 18, wherein the activity level is further determined by:

calculating an absolute value of the filtered body acceleration data,

adding absolute magnitude values of the filtered body acceleration data, and

passing the added absolute magnitude values through a low-pass filter to output the activity level.

20. The computer readable medium of claim 11, wherein each of X, Y and Z acceleration data are passed through a band pass filter.