Methods and systems for predicting hypovolemic hypotensive conditions resulting from bradycardia behavior using a pulse volume waveform

• US 10,568,583 B2
• Filed: 06/11/2014
• Issued: 02/25/2020
• Est. Priority Date: 06/11/2013
• Status: Active Grant

• Current Assignee: Intelomed, Inc.
• Original Assignee: Intelomed, Inc.
• Inventors: Brumfield, Anne M., Berkow, Jan K.
• Primary Examiner(s): Natnithithadha, Navin
• Assistant Examiner(s): Shostak, Andrey
• Application Number:
  US14/302,411
• Publication Number:
  US 20140364750A1
• Time in Patent Office: 2,085 Days
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/02455 provided with high/low alarm devices
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• 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 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/7214 using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• 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 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method for identifying cardiac bradiacardia behavior may include acquiring pulse volume wave data from a sensor associated with a patient, and calculating metrics associated with peaks detected therein. The metrics may include changes in peak amplitudes of pulse volume peaks and in the times of occurrence of pulse volume peaks. Alternative metrics may include changes in frequency domain parameters derived from the time domain pulse volume wave data. Peak amplitude values may be compared to an amplitude baseline, and differences in successive peak occurrence times may be compared to a time baseline. Cardiac bradycardia behavior may be identified by a combination of a decrease in the pulse volume peak amplitude and an increase in successive peak occurrence times. A system to implement the method may include a computing device in data communication with a photo-plethysmograph. Alternative sensors may include a blood pressure cuff and an ECG device.

Claims

1. A method for predicting a hemodynamic instability condition for a patient resulting from cardiac behavior, the method comprising:

receiving, by a computing device, a biological signal emulating an arterial pulse wave from a sensor in data communication with a human body;

performing a frequency spectrum analysis on the biological signal in a plurality of data windows over time and generating a frequency spectrum having a plurality of spectral peaks at frequencies in the frequency spectrum, each with a spectral power;

evaluating the frequency spectrum and determining, by the computing device, a plurality of pulse rate metrics from the biological signal based on the spectral peaks in the spectrum analysis over the plurality of data windows;

determining, by the computing device, a plurality of pulse strength metrics from the biological signal based on a spectral power of the spectral peaks in the spectrum analysis over the plurality of data windows;

determining a pulse rate baseline and a pulse strength baseline based on the pulse rate metrics and on the pulse strength metrics, respectively, for early data windows;

determining, by the computing device, a plurality of pulse rate differences, wherein each pulse rate difference is determined from pulse rate metrics of data windows subsequent to the early data windows and the pulse rate baseline;

determining, by the computing device, a plurality of pulse strength differences, wherein each pulse strength difference is determined from pulse strength metrics of data windows subsequent to the early data windows and the pulse strength baseline;

determining a threshold associated with the pulse strength differences;

determining a threshold associated with the pulse rate differences;

comparing at least one anomalous pulse rate difference with the pulse rate difference threshold to determine when the pulse rate difference decreases below the pulse rate threshold;

comparing at least one anomalous pulse strength difference with the pulse strength difference threshold to determine when the pulse strength difference decreases below the pulse strength threshold; and

identifying, by the computing device, the hemodynamic instability condition resulting from cardiac behavior in the human body based upon the simultaneous decrease of the at least one anomalous pulse rate difference and the at least one anomalous pulse strength difference below the respective thresholds; and

upon the identification of the hemodynamic instability condition alerting a caregiver with an output device coupled with the computing device to take action to prevent trauma to the patient.

2. The method of claim 1, wherein receiving a biological signal emulating an arterial pulse wave from a sensor comprises receiving a biological signal emulating an arterial pulse wave from one or more of a plethysmograph, a photoplethysmograph, a transmittance photo-optic sensor, a reflective photo-optic sensor, a pressure transducer, a strain gauge, an ultrasound device, a radar device, a sphygmomanometer, and an ECG device.

3. The method of claim 1, wherein determining a pulse rate metric from the biological signal comprises:

filtering, by the computing device, one or more spectral peaks from the spectrum analysis;

identifying, by the computing device, a spectral peak having a central frequency of about a pulse rate of the human body from the spectrum analysis; and

identifying, by the computing device, a spectral peak having a central frequency of about a respiration rate of the human body from the spectrum analysis.

4. The method of claim 1, further comprising determining, by the computing device, the data window from the respiration rate.

5. The method of claim 1, wherein determining a pulse strength metric from the biological signal comprises:

filtering, by the computing device, one or more spectral peaks from the spectrum analysis;

identifying, by the computing device, a spectral peak having a central frequency of about a pulse rate from the spectrum analysis;

identifying, by the computing device, a spectral peak having a central frequency of about a respiration rate from the spectrum analysis; and

identifying, by the computing device, a spectral power of the spectral peak having a central frequency of about a pulse rate of the human body.

6. The method of claim 5, further comprising determining, by the computing device, the data window from the respiration rate.

7. The method of claim 1, wherein determining the pulse rate baseline comprises:

identifying a plurality of signal peaks within a data window of the biological signal, wherein the data window includes a period of a normative cardiac rhythm;

identifying a time occurrence for each of the plurality of signal peaks;

determining a plurality of time differences, wherein each time difference is determined from a first time occurrence of the first peak and a second time occurrence of a second peak;

determining an average time difference from the plurality of time differences;

and determining an inverse of the average time difference.

8. The method of claim 1, wherein determining the pulse strength baseline comprises:

performing the frequency spectrum analysis of a portion of the biological signal within an initial plurality of windows that include a period of a normative cardiac rhythm of the human body;

filtering one or more spectral peaks from the spectrum analysis;

identifying a spectral peak having a central frequency of about a pulse rate of the human body from the frequency spectrum analysis; and

determining the pulse strength baseline from a spectral power of the identified spectral peak.

9. The method of claim 1, wherein determining the pulse strength baseline comprises:

performing the frequency spectrum analysis within an initial plurality of data windows, wherein each of the initial plurality of data windows includes a period of a normative cardiac rhythm of the human body;

filtering one or more of the plurality of spectral peaks, each of the plurality of spectral peaks being obtained from one of the frequency spectrum analyses over the initial plurality of data windows;

identifying another plurality of spectral peaks, each spectral peak having a central frequency of about a pulse rate of the human body from one of the spectrum analyses over the initial plurality of data windows; and

determining the pulse strength baseline from an average of a spectral powers, each spectral power being determined from one of the plurality of identified spectral peaks.

10. The method of claim 1, wherein determining the pulse rate threshold comprises subtracting a pulse rate factor times the pulse rate baseline from the pulse rate baseline.

11. The method of claim 10, wherein subtracting a pulse rate factor from the pulse rate baseline comprises subtracting a pulse rate factor greater than zero and less than or equal to 1 times the pulse rate baseline from the pulse rate baseline.

12. The method of claim 10, wherein subtracting a pulse rate factor from the pulse rate baseline comprises subtracting a value of about 0.15 times the pulse rate baseline from the pulse rate baseline.

13. The method of claim 10, wherein subtracting a pulse rate factor from the pulse rate baseline comprises subtracting a pulse rate factor times the pulse rate baseline from the pulse rate baseline, wherein the pulse rate factor is stored in a library of pulse rate baseline factors.

14. The method of claim 1, wherein determining the pulse strength threshold comprises subtracting a pulse strength factor times the pulse strength baseline from the pulse strength baseline.

15. The method of claim 14, wherein determining the pulse strength threshold comprises subtracting a pulse strength factor greater than zero and less than or equal to 1 times the pulse strength baseline from the pulse strength baseline.

16. The method of claim 14, wherein determining the pulse strength threshold comprises subtracting a pulse strength factor of about 0.15 times the pulse strength baseline from the pulse strength baseline.

17. The method of claim 14, wherein subtracting a pulse strength factor from the pulse strength baseline comprises subtracting a pulse strength factor times the pulse strength baseline from the pulse strength baseline, wherein the pulse strength factor is stored in a library of pulse strength baseline factors.

18. The method of claim 1, further comprising displaying, by the computing device on an output device, a representation of a portion of the biological signal along with at least one annotation identifying the hemodynamic instability condition.

19. The method of claim 18, wherein displaying the representation of the portion of the biological signal comprises updating the representation of the portion of the biological signal over time.

20. The method of claim 18, wherein the annotation identifying the hemodynamic instability condition is one or more of a hypovolemia indicator and a hypotensive indicator.

21. The method of claim 1, further comprising issuing, by the computing device, a warning that a hemodynamic instability condition indicates an emergent condition associated with the human body.

22. A system for predicting a hemodynamic instability condition for a patient resulting from cardiac behavior, the system comprising:

at least one sensor configured for data communication with a human body, the at least one sensor configured to receive a biological signal emulating an arterial pulse wave from a human body;

a computing device in operable communication with the at least one sensor;

a non-transitory, computer-readable storage medium in operable communication with the computing device;

an input device in operable communication with the computing device;

an output device in operable communication with the computing device;

wherein the computer-readable storage medium contains one or more programming instructions that, when executed, cause the computing device to;

receive a biological signal emulating an arterial pulse wave from the sensor;

perform a frequency spectrum analysis on the biological signal in a plurality of data windows over time and generating a frequency spectrum having a plurality of spectral peaks at frequencies in the frequency spectrum, each with a spectral power;

evaluate the frequency spectrum and determine a plurality of pulse rate metrics from the biological signal based on the spectral peaks in the spectrum analysis over the plurality of data windows;

determine a plurality of pulse strength metrics from the biological signal based on a spectral power of the spectral peaks in the spectrum analysis over the plurality of data windows;

determine a pulse rate baseline and a pulse strength baseline based on the pulse rate metrics and on the pulse strength metrics, respectively, for early data windows;

determine a plurality of pulse rate differences, wherein each pulse rate difference is determined from pulse rate metrics of data windows subsequent to the early data windows and the pulse rate baseline;

determine a plurality of pulse strength differences, wherein each pulse strength difference is determined from pulse strength metrics of data windows subsequent to the early data windows and the pulse strength baseline;

determine a threshold associated with the pulse strength differences;

determine a threshold associated with the pulse rate differences;

compare at least one anomalous pulse rate difference with the pulse rate difference threshold to determine when the pulse rate difference decreases below the pulse rate threshold;

compare at least one anomalous pulse strength difference with the pulse strength difference threshold to determine when the pulse strength difference decreases below the pulse rate threshold;

identify a hemodynamic instability condition resulting from cardiac behavior in the human body based upon the simultaneous decrease of the at least one anomalous pulse rate difference and the at least one anomalous pulse strength difference below the respective thresholds; and

upon the identification of the hemodynamic instability condition alerting a caregiver with the output device coupled with the computing device to take action to prevent trauma to the patient.