ELECTROCARDIOGRAM DERIVED RESPIRATION SIGNAL FOR PARASYMPATHETIC AND SYMPATHETIC MONITORING DEVICES

• US 20130274616A1
• Filed: 11/02/2012
• Published: 10/17/2013
• Est. Priority Date: 11/04/2011
• Status: Abandoned Application

• Current Assignee: The Ansar Group Incorporated
• Original Assignee: The Ansar Group Incorporated
• Inventors: Colombo, Joseph, BHATKAR, Viprali V., Welch, Robert
• Application Number:
  US13/667,769
• Publication Number:
  US 20130274616A1
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/346 Analysis of electrocardiograms
• CPC Class Codes: A61B 5/347 Detecting the frequency distribution of signals
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system

Abstract

The invention presents a method for deriving respiratory data from single lead ECG recordings for monitoring the autonomic nervous system, specifically the parasympathetic and sympathetic nervous systems independently and simultaneously. The ECG derived respiration for ANS monitoring devices generally includes a method for non-invasive monitoring of the respiratory activity for the assessment of the parasympathetic and sympathetic (P&S) branches of the autonomic nervous system. The EDR signal is used to analyze and assess the individual activities of, and interactions between the sympathetic and parasympathetic divisions of the ANS. The present invention applies a QRS peak detection algorithm to ECG signal. The peak amplitudes and respective time locations are then used to generate the respiration signal. The EDR provides an approximate but reliable estimate of the respiratory activity. The utility of the algorithm is tested for the P&S monitoring for the various tasks such as Normal Breathing (Baseline), Deep Breathing, Valsalva Maneuvers, and Standing, as well as for normal subjects, and ill and geriatric patients.

Claims

1. A method for non-invasive monitoring of the respiratory activity for the assessment of the parasympathetic and sympathetic branches of the autonomic nervous system comprising the concurrent steps of:

a) deriving respiratory activity from the measured ECG signal; and

b) using ECG derived respiration (EDR) with heart rate to analyze sympathetic and parasympathetic divisions of the ANS.

2. A method set forth in step 1, wherein step a) further comprising the steps of:

a) detecting QRS complex from measured ECG;

b) determining R-peak amplitude;

c) interpolating peak amplitudes at the down-sampling rate of 4 Hz with respect to peak amplitude time locations; and

d) obtaining EDR estimate after low pass filtering the interpolated signal.

3. A method set forth in step 1, wherein step b) further comprising the steps of:

a) computing a first power spectrum from EDR signal;

b) determining the fundamental respiratory frequency from the first power spectrum;

c) obtaining instantaneous heart rate signals from ECG signals; and

d) computing a second power spectrum from instantaneous heart rate signals.

4. A method set forth in step 1, wherein step b) further comprising the steps of

a) locating said fundamental respiratory frequency from the first power spectrum in the second power spectrum;

b) identifying a respiratory frequency range around fundamental respiratory frequency in the second power spectrum;

c) computing a respiration frequency area from second power spectrum; and

d) computing a low frequency area from second power spectrum.

5. A method set forth in step 2, wherein step a) further comprising the steps of:

a) a first filtering of the raw ECG waveform to reduce influence of muscle noise, 60 Hz interference, baseline wander, and T-wave interference;

b) a second filtering to derive the QRS complex slope information;

c) a third filtering to identify the positive and amplified peaks; and

d) a fourth filtering to obtain an approximate QRS wave.

6. A method set forth in step 2, wherein step b) further comprising the steps of:

a) applying a filter to remove ripples and multiple noise peaks; and

b) computing R-peak amplitudes and their time locations.

7. A method set forth in step 4, wherein step c) further comprising the step of:

a) using respiratory frequency area (RFa) to determine a level of parasympathetic activity.

8. A method set forth in step 4, wherein step d) further comprising the step of:

a) using low frequency area (LFa) to determine a level of sympathetic activity.

9. A method set forth in step 1 wherein step b) further comprising the step of:

a) using the ratio of said level of sympathetic activity and said level of parasympathetic activity to determine a level of sympathovagal balance.

10. A method set forth in step 7, wherein step a) comprising the step of:

a) comparing said ratio with a set of existing standards.