METHOD AND APPARATUS FOR DETERMINING HEART RATE VARIABILITY USING WAVELET TRANSFORMATION

• US 20120123232A1
• Filed: 12/16/2009
• Published: 05/17/2012
• Est. Priority Date: 12/16/2008
• Status: Abandoned Application

• Current Assignee: JB IP Acquisitions LLC, Virginia Commonwealth University
• Original Assignee: JB IP Acquisitions LLC
• Inventors: Andre, David, Stivoric, John M., Farringdon, Jonathan, Boehmke, Scott K., Kovacs, Gregory, Kasabach, Christopher, Pelletier, Raymond, Najarian, Kayvan, Ward, Kevin, Vyas, Nisarg, Teller, Eric, Gabarro, James, Ji, Soo-Yeon, Raoff, Abel Al
• Application Number:
  US13/140,165
• Publication Number:
  US 20120123232A1
• Time in Patent Office: Days
• US Class Current: 600/345
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0833 Measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: A61B 5/4875 Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

The present invention relates to advanced signal processing methods including digital wavelet transformation to analyze heart-related electronic signals and extract features that can accurately identify various states of the cardiovascular system. The invention may be utilized to estimate the extent of blood volume loss, distinguish blood volume loss from physiological activities associated with exercise, and predict the presence and extent of cardiovascular disease in general.

Claims

1. An apparatus for detecting and displaying a mammalian ECG waveform, comprising:

a sensor having at least two electrodes adapted to be worn on a mammalian body, the first of said electrodes mounted to detect a first aspect of heart-related electronic signals at a first location within an equivalence region of said body, the second of said electrodes mounted to detect a second different aspect of said heart-related electronic signals at a second location within said equivalence region;

a processing facility in electronic communication with said sensor, said processing facility receiving said first aspect of said heart-related electronic signals from said first electrode and said second aspect of said heart-related electronic signals from said second electrode, said processing facility applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals, said processing facility further deriving an ECG waveform having P, Q, R, S and T components from said heart-related electronic signals utilizing a wavelet transformation analysis; and

a display device which illustrates at least one of said P and T components of said derived ECG waveform.

2. The apparatus of claim 1, further comprising a memory circuit in electronic communication with said processing facility which stores at least one of said heart-related electronic signals and said mathematical operations.

3. The apparatus of claim 1, wherein said processing facility modifies said at least one mathematical operation in accordance with said derivation of said ECG waveform such that such that said at least one modified mathematical operation is consistently equivalent to an independently measured ECG waveform within a defined tolerance range.

4. The apparatus of claim 1, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

5. The apparatus of claim 1, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials (EMG, EEG), eye movement, non-invasive Korotkuff sounds, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors and UV sensitive photo cells.

6. The apparatus of claim 1 wherein the QRS complex of said ECG waveform is derived separately from said P and T components.

24. The apparatus of claim 1 wherein a series of said at least one time intervals are compiled to establish a variability parameter associated with said derived ECG waveform.

7. A method for detecting and displaying a mammalian ECG waveform, comprising:

associating a sensor having at least two electrodes with the body of the mammalian individual;

continuously collecting physiological data related to a first aspect of heart-related electronic signals at a first location within an equivalence region of said body, and a second different aspect of said heart-related electronic signals at a second location within said equivalence region for a period of time;

applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform having P, Q, R, S and T components from said heart-related electronic signals utilizing a wavelet transformation analysis; and

displaying at least one of said P and T components of said derived ECG waveform.

8. The method of claim 7, wherein said at least one mathematical operation for the derivation of said ECG waveform is modified in conjunction with an independently measure ECG waveform such that such that said at least one modified mathematical operation is consistently equivalent to said independently measured ECG waveform within a defined tolerance range.

9. The method of claim 7, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

10. The method of claim 7, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials eye movement, non-invasive Korotkuff sounds, body impedance, body movement, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors, UV sensitive photo cells.

11. The method of claim 7 wherein the QRS complex of said ECG waveform is derived separately from said P and T components.

12. A method for detecting and displaying a mammalian ECG waveform, comprising:

associating a sensor having at least two electrodes with the body of the mammalian individual;

continuously collecting physiological data related to a first aspect of heart-related electronic signals at a first location within an equivalence region of said body, and a second different aspect of said heart-related electronic signals at a second location within said equivalence region for a period of time;

applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform from said heart-related electronic signals;

comparing said derived ECG waveform with a corresponding actual ECG waveform from said individual;

modifying said at least one mathematical operation for defining said association of said first and second aspects of said heart-related signals with an ECG waveform to more accurately associate said heart-related aspects of said heart-related signals with an ECG waveform;

applying said modified at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform having P, Q, R, S and T components from said heart-related electronic signals utilizing a wavelet transformation analysis; and

displaying at least one of said P and T components of said derived ECG waveform.

13. The method of claim 12, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

14. The method of claim 12, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials eye movement, non-invasive Korotkuff sounds, body impedance, body movement, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors and UV sensitive photo cells.

15. The method of claim 12 wherein the QRS complex of said ECG waveform is derived separately from said P and T components.

16. The method of claim 12, further comprising the step of simulating critical care-related conditions in a mammalian subject to facilitate said derivation of said ECG waveform.

17. The method of claim 16 wherein said critical care-related conditions include at least one of shock, significant blood loss and significant decrease in blood pressure.

18. The method of claim 16 wherein said simulation of said critical care-related conditions is achieved through the use of lower body negative pressure being applied to the body of a mammalian subject.

19. An apparatus for detecting and analyzing a mammalian ECG waveform, comprising:

a sensor having at least two electrodes adapted to be worn on the mammalian body, the first of said electrodes mounted to detect a first aspect of heart-related electronic signals at a first location within an equivalence region of said body, the second of said electrodes mounted to detect a second different aspect of said heart-related electronic signals at a second location within said equivalence region;

a processing facility in electronic communication with said sensor, said processing facility receiving said first aspect of said heart-related electronic signals from said first electrode and said second aspect of said heart-related electronic signals from said second electrode, said processing facility applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals, said processing facility further deriving an ECG waveform having a plurality of repeating, recognizable wave components from said heart-related electronic signals utilizing a wavelet transformation analysis and identifying at least one time interval between said plurality of repeating, recognizable wave components; and

a display device which identifies said at least one time interval.

20. The apparatus of claim 19, further comprising a memory circuit in electronic communication with said processing facility which stores at least one of said heart-related electronic signals and said mathematical operations.

21. The apparatus of claim 19, wherein said processing facility modifies said at least one mathematical operation in accordance with said derivation of said ECG waveform such that such that said at least one modified mathematical operation is consistently equivalent to an independently measured ECG waveform within a defined tolerance range.

22. The apparatus of claim 19, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

23. The apparatus of claim 19, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials (EMG, EEG), eye movement, non-invasive Korotkuff sounds, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors and UV sensitive photo cells.

25. A method for detecting and analyzing a mammalian ECG waveform, comprising:

associating a sensor having at least two electrodes with the body of a mammalian individual;

continuously collecting physiological data related to a first aspect of heart-related electronic signals at a first location within an equivalence region of said body, and a second different aspect of said heart-related electronic signals at a second location within said equivalence region for a period of time;

applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform having a plurality of repeating, recognizable wave components from said heart-related electronic signals utilizing a wavelet transformation analysis and identifying at least one time interval between said plurality of repeating, recognizable wave components; and

reporting said at least one time interval.

26. The method of claim 25, wherein said at least one mathematical operation for the derivation of said ECG waveform is modified in conjunction with an independently measure ECG waveform such that such that said at least one modified mathematical operation is consistently equivalent to said independently measured ECG waveform within a defined tolerance range.

27. The method of claim 25, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

28. The method of claim 25, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials eye movement, non-invasive Korotkuff sounds, body impedance, body movement, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors and UV sensitive photo cells.

29. The apparatus of claim 25 wherein a series of said at least one time intervals are compiled to establish a variability parameter associated with said derived ECG waveform.

30. A method for detecting and analyzing a mammalian ECG waveform, comprising:

associating a sensor having at least two electrodes with the body of the mammalian individual;

continuously collecting physiological data related to a first aspect of heart-related electronic signals at a first location within an equivalence region of said body, and a second different aspect of said heart-related electronic signals at a second location within said equivalence region for a period of time;

applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform from said heart-related electronic signals;

comparing said derived ECG waveform with a corresponding actual ECG waveform from said individual;

modifying said at least one mathematical operation for defining said association of said first and second aspects of said heart-related signals with an ECG waveform to more accurately associate said heart-related aspects of said heart-related signals with an ECG waveform;

applying said modified at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform having a plurality of repeating, recognizable wave components from said heart-related electronic signals utilizing a wavelet transformation analysis and identifying at least one time interval between said plurality of repeating, recognizable wave components; and

reporting said at least one time interval.

31. The method of claim 30, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

32. The method of claim 30, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials eye movement, non-invasive Korotkuff sounds, body impedance, body movement, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors, UV sensitive photo cells.

33. The method of claim 30, further comprising the step of simulating critical care-related conditions in a mammalian subject to facilitate said derivation of said ECG waveform.

34. The method of claim 33 wherein said critical care-related conditions include at least one of shock, significant blood loss and significant decrease in blood pressure.

35. The method of claim 33 wherein said simulation of said critical care-related conditions is achieved through the use of lower body negative pressure being applied to the body of a mammalian subject.

36. A method for detecting and analyzing a mammalian ECG waveform, comprising:

associating a sensor having at least two electrodes with the body of a human individual;

continuously collecting physiological data related to a first aspect of heart-related electronic signals at a first location and a second different aspect of said heart-related electronic signals at a second location for a period of time;

applying at least one mathematical operation defining the association of said first and second aspects of said heart-related electronic signals with an ECG waveform to said first and second aspects of said heart-related electronic signals;

deriving an ECG waveform having a plurality of repeating, recognizable wave components from said heart-related electronic signals utilizing a wavelet transformation analysis and identifying at least one time interval between said plurality of repeating, recognizable wave components;

accurately deriving a critical care parameter from said at least one time interval; and

reporting at least one of said critical care parameter and said at least one time interval.

37. The method of claim 36, wherein the heart-related signals are selected from the group consisting of:

electrical activity of the heart over time, respiration rate, skin temperature, body core temperature, heat flow, galvanic skin response, electrical activity of muscles, bioimpedence, optical plethysmography, piezo motions, the spontaneous electrical activity of the brain, eye movement, blood pressure, body fat, activity, oxygen consumption, glucose level, carbon dioxide level, NADH level, tissue hemoglobin oxygen saturation level, body position, muscle pressure, UV radiation absorption, and lactate level.

38. The method of claim 36, wherein the derivation of said ECG waveform further comprises at least one of:

measuring skin surface potential, chest volume change, surface temperature probe, esophageal or rectal probe, heat flux, skin conductance, skin surface potentials eye movement, non-invasive Korotkuff sounds, body impedance, body movement, body impedance, body movement, oxygen uptake, electrochemical measurement, optical spectroscopy, fluorescence spectroscopy, mercury switch array, think film piezoelectric sensors and UV sensitive photo cells.

39. The method of claim 36 wherein said critical care-related parameter is selected from the group consisting of ventricular fibrillation, arrhythmia, atria abnormalities, blood volume loss, hemorrhagic shock and cardiovascular disease hemorrhage (nontraumatic), traumatic hemorrhage, acute and chronic heart failure including myocardial infarction and acute arhythmias, cardiac arrest and cardiogenic shock, bacterial infection, viral infection, fungal infection, pneumonia, sepsis, septic shock, wounds, burns, hyper and hypothryoid, adrenal insufficiency, diabetic ketoacidosis, hyperthermia, hypothermia, preeclampsia, eclampsia, seizures, status epilepticus, drowning, acute respiratory failure, pulmonary embolism, traumatic brain injury, spinal cord injury, stroke, cerebral aneurysm;

limb ischemia, coagulopathies, acute neuromuscular disease/failure, acute poisonings, vasoocclusive crisis and tumor lysis syndrome.