Adaptive selection of digital ECG filter

• US 9,636,029 B1
• Filed: 03/14/2013
• Issued: 05/02/2017
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: Vital Connect, Inc.
• Original Assignee: Vital Connect, Inc.
• Inventors: Narasimhan, Ravi, Ferdosi, Nima, Nazari, Nersi
• Primary Examiner(s): Kahelin, Michael
• Assistant Examiner(s): PHAM, MINH DUC GIA
• Application Number:
  US13/828,544
• Time in Patent Office: 1,510 Days
• Field of Search: 600508, 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 for filtering a detected ECG signal using a plurality of digital filters, the method comprising:

calculating a kurtosis of a detected ECG signal as a statistical quality indicator;

calculating a mid-beat signal-to-noise ratio (SNR) by analyzing successive QRS peaks of the detected ECG signal;

calculating a quality metric for a plurality of digital filters by combining the statistical quality indicator and the mid-beat signal-to-noise ratio (SNR),

wherein the statistical quality indicator and the mid-beat SNR quality indicator are combined via a weighted linear combination; and

adaptively selecting one of the plurality of digital filters by comparing the calculated quality metric to a minimum quality metric threshold and by determining a cutoff frequency setting of the one of the plurality of digital filters to prevent erratic switching between the plurality of digital filters.

2. The method of claim 1, wherein the plurality of digital filters comprise a plurality of parallel digital band-pass filters with varying high-pass cutoff frequencies.

3. The method of claim 1, further comprising:

utilizing an output of the adaptively selected digital filter to calculate an RR interval of the detected ECG signal.

4. The method of claim 1, wherein the calculating the quality metric is only on one of the plurality of digital filters at a time to conserve power consumption.

5. The method of claim 1, further comprising:

calculating an activity level using a microelectromechanical systems (MEMS) device embedded within a wireless sensor device to measure noise of the detected ECG signal.

6. The method of claim 5, further comprising:

providing notifications of ECG signal quality and recommended actions based upon the calculated Quality Metric and the calculated activity level.

7. The method of claim 1, wherein the calculating of the mid-beat SNR further comprises:

calculating a ratio of signal power over noise power.

8. The method of claim 7, wherein the noise power is calculated as a variance of the detected ECG signal over a predetermined time period in a middle of a TP segment that is averaged over a plurality of beats.

9. The method of claim 8, wherein the signal power is calculated as an average of RS amplitude squared over the plurality of beats.

10. The method of claim 1, wherein the statistical quality indicator is weighted less during the calculation of the quality metric when the detected ECG signal is of a higher quality.

11. The method of claim 1, wherein the mid-beat SNR is weighted less during the calculation of the quality metric when the detected ECG signal is of a lower quality.