SYSTEMS FOR SAFE AND REMOTE OUTPATIENT ECG MONITORING

• US 20090171227A1
• Filed: 10/16/2006
• Published: 07/02/2009
• Est. Priority Date: 10/14/2005
• Status: Active Grant

• Current Assignee: Medicalgorithmics Ltd. (Medicalgorithmics S.A.)
• Original Assignee: Medicalgorithmics Ltd. (Medicalgorithmics S.A.)
• Inventors: Dziubinski, Marek, Baranowski, Rafal, Piotrowicz, Ewa, Piotrowicz, Ryszard
• Granted Patent: US 8,818,496 B2
• Time in Patent Office: Days
• US Class Current: 600/516
• 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 5/1118 Determining activity level
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/333 Recording apparatus specially adapted therefor
• CPC Class Codes: A61B 5/346 Analysis of electrocardiograms
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/36 Detecting PQ interval, PR interval or QT interval
• 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 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• 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 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• 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 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A system and method providing outpatient ECG monitoring and safe home based cardiac tele-rehabilitation. The system includes a recordation module for recording ECG signals using at least one lead, a tele-rehabilitation module for home based exercise management for a patient'"'"'s recovery from a heart disease, the tele-rehabilitation module including a processing module for recognizing erroneous data from the ECG signals and an analysis module for calculating beat-to-beat annotations and determining if an ECG event and/or if a QT interval duration change has occurred. The system can include an exercise module for guiding the patient during an exercise session, a visual display that informs the patient to start and/or to stop the tele-rehabilitation exercise, a visual display and/or audible signal that informs the patient of an incoming or a missed tele-rehabilitation exercise session, and/or a communication module for transmitting/receiving data between the a cardiac tele-rehabilitation module and a physician/monitoring centre.

Claims

1. A limited-lead electrocardiogram (ECG) system for remote outpatient cardiac monitoring and tele-rehabilitation, comprising:

a recordation module for recording ECG signals using at least one lead;

a cardiac tele-rehabilitation module for home based exercise management for a patient'"'"'s recovery from a heart disease, the cardiac tele-rehabilitation module including;

a processing module for recognizing erroneous data from the ECG signals;

an analysis module for determining if an ECG event has occurred;

a communication module for reporting the processed ECG signals and/or other detected ECG events including streaming all of the annotations and information describing each ECG beat.

2. A system of claim 1, further comprising an exercise module for guiding the patient during an exercise session.

3. A system of claim 2, further comprising a visual display that informs the patient to start and/or to stop the tele-rehabilitation exercise.

4. A system of claim 2, further comprising a visual display and/or audible signal that informs the patient of an incoming or a missed tele-rehabilitation exercise session.

5. A system of claim 4, wherein the information provided to the patient includes:

an indication to intensify the exercise because the patient'"'"'s heart rate is below a predefined threshold;

an indication to decrease the exercise intensity because the patient'"'"'s heart rate is above a predefined threshold;

an indication to stop the exercise because a significant cardiac event has occurred; and

an indication to contact the patient'"'"'s physician.

6. A system of claim 1, further comprising a communication module for transmitting/receiving data between the a cardiac tele-rehabilitation module and a physician/monitoring centre.

7. A system of claim 6, wherein the physician/monitoring centre includes a control module for controlling/reprogramming the cardiac tele-rehabilitation module.

8. A system of claim 7, wherein the control module allows the patient'"'"'s physician, or a medical personnel assign to the patient, to control the patient'"'"'s tele-rehabilitation exercise session.

9. A system of claim 8, wherein control of the tele-rehabilitation exercise session is based on reviewing received data from the cardiac tele-rehabilitation module, the received data including an ECG analysis of the patient and a data describing the patient'"'"'s physical and mental condition.

10. A system of claim 9, wherein the data describing patient'"'"'s physical and mental condition include a blood pressure of the patient, a body weight of the patient, a stress information of the patient;

a mood information of the patient, and a pharmacotherapy information of the patient.

11. A system of claim 10, wherein the data describing patient'"'"'s physical and mental condition is input into the cardiac tele-rehabilitation module by the patient, by external measuring devices, and/or a person at the physician/monitoring centre.

12. A system of claim 7, wherein the control module allows for setting or modifying the tele-rehabilitation program.

13. A system of claim 12, wherein the settings or modifications include a minimum heart rate exercise threshold, a maximum heart rate exercise threshold, a number of exercise sessions during the day, a session duration, a number of exercises during each session, and exercise and pause duration.

14. The system of claim 1, wherein the ECG event has occurred is based on at least one of calculating beat-to-beat arrhythmia annotations, calculating a ST segment elevation, a TWA level, and a QT interval, estimating a noise level and an ADC network level surrounding each beat, and/or detecting electrodes problems.

15. The system of claim 14, wherein the detected ECG event is communicated to medical personnel and/or a wearer of the recordation module.

16. The system of claim 14, wherein the processed ECG signals are segmented and then the selected ECG strips related to the detected events are communicated to medical personnel.

17. The system of claim 14, further comprising a server for receiving the processed selected ECG signal strips and/or the ECG event from the reporting module.

18. The system of claim 17, wherein the server receives the processed ECG signal strips and/or the ECG event from the reporting module for a plurality of patients.

19. The system of claim 18, wherein the server directs the processed ECG signals and/or the ECG event from the reporting module for the plurality of patients to medical personnel responsible for a respective patient.

20. The system of claim 19, wherein the medical personnel have direct access to a patient'"'"'s processing module and/or analysis module through the server.

21. The system of claim 20, wherein the direct access allows the medical personnel to remotely alter parameters stored in the patient'"'"'s processing module and/or analysis module.

22. The system of claim 1, wherein the processing module includes an analysis algorithm recognizing erroneous data from the ECG signals.

23. The system of claim 18, wherein the analysis algorithm includes a noise and distortion detection sub-algorithm (NDDA) for detecting noisy and non-linearly distorted ECG fragments and a sub algorithm (SPA) for elimination of ADC interference.

24. The system of claim 19, wherein the NDDA further estimates a broad band noise energy level of the signal surrounding each beat and SPA estimates ADC network level surrounding each beat.

25. A method for analyzing limited-lead electrocardiogram (ECG) system signals, comprising:

recording ECG signals using at least one lead;

performing cardiac tele-rehabilitation, wherein performing cardiac tele-rehabilitation includes;

recognizing erroneous data from the ECG signals to form a pre-classified ECG signal; and

determining if an ECG event has occurred from the pre-classified signal and calculating annotations for every ECG beat.

26. The method of claim 25, further comprising reporting the calculated annotations for every ECG beat and/or the ECG events and/or noise level for each ECG beat and ADC network interference for each ECG beat.

27. The method of claim 26, wherein the ECG event is reported to medical personnel or a wearer of the recordation module.

28. The method of claim 26, wherein the calculated annotations representing each ECG beat are segmented and then reported to medical personnel.

29. The method of claim 26, further comprising receiving the annotations for each ECG beat and/or the ECG event at a remote location.

30. The method of claim 25, wherein the erroneous data from the ECG signals is detected using an analysis algorithm.

31. The method of claim 30, wherein the analysis algorithm includes a noise and distortion detection sub-algorithm (NDDA) for detecting noisy and non-linearly distorted ECG fragments and a ADC network interference removing sub-algorithm (SPA).

32. The method of claim 31, wherein the NDDA further estimates a broad band noise energy level of the signal.

33. The method of claim 25, wherein the pre-classified signal is analyzed using a beat classification algorithm and/or an arrhythmia detection algorithm.

34. The method of claim 33, wherein the analyzed signal is verified using a detection evaluation correction algorithm.

35. The method of claim 33, wherein the beat classification algorithm and the arrhythmia detection algorithm generate the calculated annotations for every ECG beat.

36. The method of claim 35, further comprising updating an averaged normal ECG period for each new non-pathological ECG period based on the performed beat classification and arrhythmia detection.

37. The method of claim 36, wherein the averaged ECG period is used for calculating ST segment elevation and for QT interval duration difference between the averaged ECG period and the reference ECG period.

38. The method of claim 37, wherein the reference ECG period is an averaged ECG period with a predetermined QT interval, allowing for QT interval determination of each new averaged ECG period based on the predetermined interval value and the current QT interval difference value.

39. The method of claim 37, wherein the QT interval difference is obtained by finding a best match of a time domain shifted T wave representation signal of the averaged ECG period and the T wave representation signal of the reference ECG period.

40. The method of claim 39, wherein the signal representations are difference signals of the averaged ECG period and the reference ECG period.

41. The method of claim 39, wherein the best match of the time domain shifted T wave representation of the averaged ECG period and the T wave representation of the reference ECG period is the maximum value of a shifted T wave matching function.

42. The method of claim 41, wherein index of the maximum value of a similarity function is interpolated to enhance the T wave shift accuracy.

43. The method of claim 42, wherein the interpolation is performed by a parabola fitting to the maximum similarity value and a surrounding values of the similarity curve.

44. The method of claim 41, wherein the maximum similarity value is the maximum of all maximum similarity values of the reference ECG period and all collected auxiliary reference ECG periods compared with a new averaged ECG period.

45. The method of claim 38, wherein auxiliary reference ECG periods are collected if a shape changing T wave occurs.

46. The method of claim 45, wherein the averaged ECG period collected is used as an auxiliary reference ECG period.

47. The method of claim 35, wherein a non-pathological ECG period is a current ECG period used for calculating a T wave alternans amplitude.

48. The method of claim 47, wherein a value of the base line level is a median value of an isoelectric line signal segment preceding a current ECG period.

49. The method of claim 48, wherein the value of a base line level deviation is a standard deviation of the current base line level and a J preceding base line level.

50. The method of claim 47, wherein a value of the isoelectric line deformation of the current ECG period is a standard deviation of a difference of the current isoelectric line, preceding the current ECG period and an isoelectric line preceding the previous ECG period.

51. The method of claim 47, wherein an unbiased current ECG period is calculated by removing a low frequency T wave shape trend from the current ECG period.

52. The method of claim 51, wherein the low frequency T trend is removed by subtracting an averaged ECG period from the current ECG period.

53. The method of claim 47, wherein a periodicity values representing each sample of the current ECG period are calculated.

54. The method of claim 53, wherein the periodicity values for n samples of the current ECG period are calculated based on n autocorrelation sequences, calculated with the use of a J consecutive unbiased ECG periods, including the current unbiased ECG period.

55. The method of claim 54, wherein a T wave amplitude is calculated based on an unbiased-averaged-difference-ECG-period.

56. The method of claim 55, wherein the unbiased-averaged-difference-ECG-period is calculated based on J/2 pairs of a J consecutive unbiased ECG periods, the preceding the current unbiased ECG period and including the current unbiased ECG period.

57. The method of claim 56, wherein the T wave alternans is a maximum of the unbiased-averaged-difference-ECG-period.

58. The method of claim 57, wherein an unbiased-averaged-difference-ECG-period is weighted by the periodicity values.

59. The method of claim 58, wherein a maximum value of the unbiased-averaged-difference-ECG-period, weighted by periodicity values and compensated by base line drift deviation values and isoelectric line deformation values is a calculated T wave alternans amplitude for the current ECG period.