ANALYTE ASSESSMENT AND ARRHYTHMIA RISK PREDICTION USING PHYSIOLOGICAL ELECTRICAL DATA

• US 20160256063A1
• Filed: 09/26/2014
• Published: 09/08/2016
• Est. Priority Date: 09/27/2013
• Status: Abandoned Application

• Current Assignee: B.G. Negev Technologies And Applications Ltd (Ben Gurion University of The Negev), Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Original Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Inventors: Dillon, John J., Sapir, Yehu, Geva, Amir, Friedman, Paul A., Bennet, Kevin E., Bruce, Charles J., Somers, Virend K., Asirvatham, Samuel J., Ackerman, Michael J., Sadot, Dan
• Application Number:
  US15/025,158
• Publication Number:
  US 20160256063A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/282 Holders for multiple electrodes for introduction into the body A61B5/287
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/35 by template matching
• 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 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/6861 Capsules, e.g. for swallowing or implanting
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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

Abstract

This document describes, among other things, a computer-implemented method that includes accessing, by a computer system, electrogram data for a patient, wherein the electrogram data is obtained using one or more leads that sense physiological electrical activity of the patient. The computer system can identify one or more waveform features from the electrogram data, and one or more correlations between values of the one or more waveform features and analyte levels. One or more estimated analyte levels in the patient are determined based on 1) the one or more waveform features identified from the electrogram data and 2) the one or more correlations. The computer system can output information related to the one or more estimated analyte levels.

Claims

1. A computer-implemented method, comprising:

accessing, by a computer system, electrogram data for a patient, wherein the electrogram data is obtained using one or more leads that sense physiological electrical activity of the patient;

identifying, by the computer system, one or more waveform features from the electrogram data;

identifying, by the computer system, one or more correlations between values of the one or more waveform features and analyte levels;

determining, by the computer system, one or more estimated analyte levels in the patient based on

     1) the one or more waveform features identified from the electrogram data and

     2) the one or more correlations; and

outputting, by the computer system, information related to the one or more estimated analyte levels.

2. The computer-implemented method of claim 1, further comprising:

before identifying the one or more waveform features, filtering the electrogram data to generate filtered electrogram data;

wherein the one or more waveform features are identified from the filtered electrogram data.

3. The computer-implemented method of claim 2, wherein the filtering includes a first filtering process comprising:

identifying R peak values in the electrogram data;

identifying intervals in the electrogram data between adjacent R peak values;

determining an average for the intervals;

identifying a portion of the intervals that are at least a threshold value above or below the average; and

removing the portion of the intervals from the electrogram data to generate the filtered electrogram data.

4. The computer-implemented method of claim 3, wherein the vector for the electrogram data comprises a PQRST complex electrogram data vector or any component thereof.

5. The computer-implemented method of claim 3, wherein the threshold value comprises a threshold percentile above or below the average.

6. The computer-implemented method of claim 3, wherein the average for the intervals is determined from only a portion of the electrogram data that is identified within a window of time from the electrogram data.

7. The computer-implemented method of claim 2, wherein the filtering includes a second filtering process comprising:

identifying R peak values for R-waves in the electrogram data;

determining an average R peak value from the identified R peak values;

identifying a portion of the R-waves with R peak values that are at least a threshold value above or below the average R peak value; and

removing the portion of the R-waves from the electrogram data to generate the filtered electrogram data.

8. The computer-implemented method of claim 7, wherein the vector for the electrogram data comprises a PQRST complex electrogram data vector or any component thereof.

9. The computer-implemented method of claim 7, wherein the threshold value comprises a threshold percentile above or below the average R peak value.

10. The computer-implemented method of claim 7, wherein the average R peak value is determined from only a portion of the electrogram data that is identified within a window of time from the electrogram data.

11. The computer-implemented method of claim 2, wherein the filtering includes a third filtering process comprising:

identifying a vector for the electrogram data;

identifying an average ECG vector;

determining a statistical covariance between the average ECG vector and the vector for the electrogram data;

determining one or more correlation coefficients for the electrogram data based on determined statistical covariance; and

removing portions of the electrogram data with corresponding correlation coefficients that are less than a threshold correlation value to generate the filtered electrogram data.

12. The computer-implemented method of claim 11, wherein the vector for the electrogram data comprises a PQRST complex electrogram data vector.

13. The computer-implemented method of claim 2, wherein the filtering includes a fourth filtering process comprising:

for a particular P wave in the electrogram data, identifying at least a threshold number of preceding P waves;

determining a mean voltage level for the preceding P waves;

adjusting the elevation of the particular P wave and portions of the electrogram data surrounding or to the left of the P wave based on the mean voltage level to generate the filtered electrogram data.

14. The computer-implemented method of claim 2, wherein the filtering includes a fifth filtering process comprising:

averaging electrogram data from the one or more leads to generate the filtered electrogram data.

15. The computer-implemented method of claim 1, wherein the one or more waveform features identified from the electrogram data includes a P-wave that precedes an R-wave in the electrogram data.

16. The computer-implemented method of claim 15, wherein the P-wave includes one or more of i) a P-wave area value comprising an area underneath the P-wave and ii) a P-wave amplitude value comprising an amplitude of the P-wave.

17. The computer-implemented method of claim 1, wherein the one or more waveform features identified from the electrogram data includes a QRS complex that comprises Q, R, and S peak points for a Q-wave, an R-wave, and an S-wave.

18. The computer-implemented method of claim 17, wherein the QRS complex includes one or more of i) a QRS area value comprising an area of a triangle formed by the Q, R, and S peak points and ii) a QRS area changes value comprising a change in the QRS area value between one or more R-waves.

19. The computer-implemented method of claim 17, wherein identification of the QRS complex from the electrogram data comprises:

identifying the R peak point for the R-wave in the electrogram data; and

identifying the S peak point for the S-wave and the Q-wave nadir for the Q-wave based on a comparison of a first order derivative of the electrogram data to a statistically defined threshold value.

20. The computer-implemented method of claim 1, wherein the one or more waveform features identified from the electrogram data includes a T-wave that proceeds after an R-wave in the electrogram data.

21. The computer-implemented method of claim 20, wherein the T-wave is divided into sections based on a relationship between i) a peak of the T-wave and ii) a beginning and an end of the T-wave.

22. The computer-implemented method of claim 20, wherein the T-wave includes one or more of i) a T-wave area value comprising an area underneath the T-wave, ii) a T-wave amplitude value comprising an amplitude of the T-wave, iii) a T-wave left slope value comprising a slope value for a left portion of the T-wave, iv) a T-wave right slope value comprising a slope value for a right portion of the T-wave, and v) a T-wave center of gravity value comprising a center point under a curve of the T-wave.

23. The computer-implemented method of claim 22, wherein the T-wave is divided into sections and the following features are determined for each of the sections:

the T-wave area value, the T-wave amplitude, the T-wave left slope value, the T-wave right slope value, and the T-wave center of gravity.

24. The computer-implemented method of claim 22, wherein determination of one or more of the T-wave right slope value and the T-wave left slope value comprises:

identifying a start and end point of the T-wave from the electrogram data;

identifying an inflection point at which a second derivative for a curve of the T-wave changes signs;

determine i) a left point that is a threshold number of samples left of the inflection point along the curve of the T-wave and ii) a right point that is a threshold number of samples right of the inflection point along the curve of the T-wave; and

determine a slope between the left point and the right point.

25. The computer-implemented method of claim 22, wherein determination of one or more of the T-wave right slope value and the T-wave left slope value comprises:

identifying a start and end point of the T-wave from the electrogram data;

determine a first derivative between a peak of the T-wave and the end point of the T-wave; and

determine a mean of a plurality of slope value samples that are derived from sample points along the first derivative.

26. The computer-implemented method of claim 22, wherein determination of one or more of the T-wave right slope value and the T-wave left slope value comprises:

identifying a start and end point of the T-wave from the electrogram data;

determine a first derivative between a peak of the T-wave and the end point of the T-wave;

determine a plurality of mean slope values, wherein each mean slope value comprises a mean of a plurality of slope values for sample points along the a curve of the T-wave, the slope values being derived from the first derivative; and

identifying a minimum of the plurality of mean slope values.

27. The computer-implemented method of claim 20, wherein identification of the T-wave from the electrogram data comprises:

selecting a size for a sliding window;

iteratively moving a position of the sliding window forward in time along the electrogram data and, at each iteration, determining an area under a curve defined by the electrogram data; and

identifying starting and ending points for the T-wave based on positions of the sliding window when on a maximum area value and a minimum area value was determined.

28. The computer-implemented method of claim 20, wherein identification of the T-wave from the electrogram data comprises:

determining a line from a T-wave peak point to a heart rate adjusted point forward in time;

evaluating vertical distances between the line and a waveform defined by the electrogram data; and

identifying a point in time on the waveform with a maximum vertical distance as the start or end point of the T-wave.

29. The computer-implemented method of claim 1, wherein the determining of the one or more estimated analyte levels comprises determining a virtual lead that indicates the one or more estimated analyte levels for the patient based on the electrogram data derived from the one or more leads that sense physiological electrical activity of the patient.

30. The computer-implemented method of claim 1, wherein identifying the one or more correlations between values of the one or more waveform features and analyte levels comprises:

transforming a data matrix representing the electrogram data for the one or more leads into a virtual lead space that indicates the one or more estimated analyte levels for the patient, the transformation of the data matrix generating one or more virtual leads that indicate analyte levels for the patient; and

statistically analyzing the one or more virtual leads to identify the one or more correlations.

31. The computer-implemented method of claim 30, wherein the transforming comprises principal component analysis (PCA) for the data matrix.

32. The computer-implemented method of claim 30, wherein the transforming comprises PCA of the data matrix and unsupervised optimal fuzzy clustering of a coefficient matrix generated from the PCA of the data matrix.

33. The computer-implemented method of claim 30, wherein the statistically analyzing comprises performing multiple linear regression or multivariate regression analysis on the one or more virtual leads.

34. The computer-implemented method of claim 1, wherein the analyte levels are selected from the group consisting of:

potassium, calcium, magnesium, phosphorous, and anti-arrhythmic drugs.

35. The computer-implemented method of claim 1, wherein the output information identifies one or more ranges that are associated with the one or more estimated analyte levels.

36. The computer-implemented method of claim 1, wherein the output information identifies whether the one or more estimated analyte levels fall within one or more ranges.

37. The computer-implemented method of claim 1, wherein the output information identifies at least a portion of the one or more estimated analyte levels.

38. The computer-implemented method of claim 1, further comprising:

recording, based on electrogram data and corresponding analyte level measurements, the one or more correlations that are specific to the patient.

39. The computer-implemented method of claim 1, further comprising:

generating an mathematically characterized template that is specific to the patient and that provides a baseline of analyte levels for the patient; and

comparing the one or more estimated analyte levels for the patient to the template to identify deviations from the template.

40. The computer-implemented method of claim 1, further comprising:

performing frequency domain analysis with regard to the electrogram data.

41. The computer-implemented method of claim 1, further comprising:

performing a wavelet transform with regard to the electrogram data.

42. The computer-implemented method of claim 1, further comprising:

modeling the electrogram data using a hidden Markov model.

43. The computer-implemented method of claim 1, further comprising:

performing linear discriminate analysis with regard to each characteristic of the electrogram data.

44. The computer-implemented method of claim 1, wherein the electrogram data is obtained from an implanted recording system.

45. The computer-implemented method of claim 44, wherein the implanted recording system comprises a dedicated system for assessing analyte levels.

46. The computer-implemented method of claim 44, wherein the implanted recording system comprises an implantable loop recorder that is capable of being used to diagnose arrhythmia or syncope.

47. The computer-implemented method of claim 44, wherein the implanted recording system is included in a pacemaker, defibrillation, or resynchronization system.

48. The computer-implemented method of claim 44, wherein the implanted recording system comprises an indwelling dialysis catheter.

49. The computer-implemented method of claim 44, wherein the implanted recording system comprises an implant.

50. The computer-implemented method of claim 49, wherein the implant is an abdominal implant, a central nervous system implant, or a vascular implant.

51. The computer-implemented method of claim 44, wherein the implanted recording system comprises an ingestable device.

52. The computer-implemented method of claim 51, wherein the ingestable device comprises an electronic capsule or tablet.

53. The computer-implemented method of claim 1, further comprising determining, based on the electrogram data, a risk that the patient will develop ventricular arrhythmias.

54. The computer-implemented method of claim 1, further comprising determining, based on the electrogram data, a risk that the patient will develop atrial fibrillation.

55. The computer-implemented method of claim 1, further comprising determining, based on the electrogram data, a risk that the patient will experience drug-induced proarrhythmia.

56. The computer-implemented method of claim 1, wherein the computer system comprises a smartphone, a tablet computing device, or a notebook computer.

59. The computer-implemented method of claim 1, further comprising determining information that characterizes the patient'"'"'s body position at a time when the electrogram data is obtained.

60. The computer-implemented method of claim 59, wherein determining the information that characterizes the patient'"'"'s body position comprises processing signals obtained from an accelerometer connected to the patient.

61. The computer-implemented method of claim 59, wherein the one or more waveform features are identified in response to determining that the patient'"'"'s body position matches a predetermined body position.

62. The computer-implemented method of claim 59, further comprising determining that the patient'"'"'s body position at the time when the electrogram data is obtained has changed from a predetermined body position, and in response to determining that the patient'"'"'s body position has changed from the predetermined body position, adjusting the one or more estimated analyte levels.

63. The computer-implemented method of claim 1, further comprising:

monitoring the patient'"'"'s heart rate; and

determining that the patient'"'"'s heart rate is within an acceptable range of a baseline heart rate,

wherein the electrogram data is accessed in response to determining that the patient'"'"'s heart rate is within the acceptable range.

64. The computer-implemented method of claim 63, wherein the acceptable range is ten beats per minute above or below the baseline heart rate.

65. The computer-implemented method of claim 1, further comprising determining that the patient'"'"'s heart rate at a time when the electrogram data is obtained deviates from a baseline heart rate, and in response to determining that the patient'"'"'s heart rate deviates from the baseline heart rate, adjusting the one or more estimated analyte levels.

66. The computer-implemented method of claim 6, wherein the window of time is defined by at least one of a start time and an end time, the start time and end time corresponding to a particular time of day.

67. The computer-implemented method of claim 6, wherein the window of time is determined based on a time when the patient'"'"'s body position or heart rate matches a baseline body position or a baseline heart rate.

68. The computer-implemented method of claim 29, wherein determining the virtual lead that indicates the one or more estimated analyte levels for the patient comprises determining a difference between adjacent unipolar electrodes in the one or more leads and comparing the difference to a signal from a local bipole.

69. The computer-implemented method of claim 1, further comprising determining a time-based derivative of the electrogram data, wherein the one or more waveform features are identified from the time-based derivative of the electrogram data.

70. The computer-implemented method of claim 39, further comprising generating, based on a determination that the one or more estimated analyte levels for the patient deviate at least a threshold amount from baseline analyte levels in the patient-specific template, an alert to notify a user of the deviation.

71. The computer-implemented method of claim 70, wherein generating the mathematically characterized template comprises drawing blood from the patient and measuring one or more components to determine the baseline of analyte levels.

72. The computer-implemented method of claim 53, wherein determining the risk that the patient will develop ventricular arrhythmias comprises determining a center of gravity or a T-wave slope based on the patient'"'"'s electrogram data.

73. The computer-implemented method of claim 1, wherein the electrogram data comprises one or more of electrocardiogram data, brain electrogram data, muscular electrogram data, myoelectrogram data, and neuro-electrogram data.

74. The computer-implemented method of claim 1, wherein the one or more leads that sense physiological electrical activity of the patient are physically attached to the patient.

57. A computer-implemented method comprising:

accessing, by a computer system, electrical signal data for a patient, wherein the electrical signal data is obtained using one or more leads that sense physiological electrical activity of the patient;

identifying, by the computer system, one or more waveform features from the electrical signal data;

identifying, by the computer system, one or more correlations between values of the one or more waveform features and analyte levels;

determining, by the computer system, one or more estimated analyte levels in the patient based on

     1) the one or more waveform features identified from the electrical signal data and

     2) the one or more correlations; and

outputting, by the computer system, information related to the one or more estimated analyte levels.

58. The computer-implemented method of claim 57, wherein the electrical signal data is selected from a group consisting of electrocardiogram (ECG) data, electroencephalography (EEG) data, and data that characterizes the patient'"'"'s response to a localized stimulation.