SYSTEMS FOR SAFE AND REMOTE OUTPATIENT ECG MONITORING

• US 20150018700A1
• Filed: 07/14/2014
• Published: 01/15/2015
• 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, Piotrowicz, Ryszard, Piotrowicz, Ewa, Baranowski, Rafal
• Granted Patent: US 9,351,652 B2
• Time in Patent Office: Days
• US Class Current: 600/515
• 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 center.

Claims

1. 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.

2. The method of claim 1, 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.

3. The method of claim 2, wherein the ECG event is reported to medical personnel or a wearer of the recordation module.

4. The method of claim 2, wherein the calculated annotations representing each ECG beat are segmented and then reported to medical personnel.

5. The method of claim 2, further comprising receiving the annotations for each ECG beat and/or the ECG event at a remote location.

6. The method of claim 1, wherein the erroneous data from the ECG signals is detected using an analysis algorithm.

7. The method of claim 6, 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).

8. The method of claim 7, wherein the NDDA further estimates a broad band noise energy level of the signal.

9. The method of claim 1, wherein the pre-classified signal is analyzed using a beat classification algorithm and/or an arrhythmia detection algorithm.

10. The method of claim 9, wherein the analyzed signal is verified using a detection evaluation correction algorithm.

11. The method of claim 9, wherein the beat classification algorithm and the arrhythmia detection algorithm generate the calculated annotations for every ECG beat.

12. The method of claim 11, further comprising updating an averaged normal ECG period for each new non-pathological ECG period based on the performed beat classification and arrhythmia detection.

13. The method of claim 12, 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.

14. The method of claim 13, 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.

15. The method of claim 13, 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.

16. The method of claim 15, wherein the signal representations are difference signals of the averaged ECG period and the reference ECG period.

17. The method of claim 15, 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.

18. The method of claim 17, wherein index of the maximum value of a similarity function is interpolated to enhance the T wave shift accuracy.

19. The method of claim 18, wherein the interpolation is performed by a parabola fitting to the maximum similarity value and a surrounding values of the similarity curve.

20. The method of claim 17, 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.

21. The method of claim 14, wherein auxiliary reference ECG periods are collected if a shape changing T wave occurs.

22. The method of claim 21, wherein the averaged ECG period collected is used as an auxiliary reference ECG period.

23. The method of claim 11, wherein a non-pathological ECG period is a current ECG period used for calculating a T wave alternans amplitude.

24. The method of claim 23, wherein a value of the base line level is a median value of an isoelectric line signal segment preceding a current ECG period.

25. The method of claim 24, 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.

26. The method of claim 23, 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.

27. The method of claim 23, wherein an unbiased current ECG period is calculated by removing a low frequency T wave shape trend from the current ECG period.

28. The method of claim 27, wherein the low frequency T trend is removed by subtracting an averaged ECG period from the current ECG period.

29. The method of claim 23, wherein a periodicity values representing each sample of the current ECG period are calculated.

30. The method of claim 29, 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.

31. The method of claim 30, wherein a T wave amplitude is calculated based on an unbiased-averaged-difference-ECG-period.

32. The method of claim 31, 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.

33. The method of claim 32, wherein the T wave alternans is a maximum of the unbiased-averaged-difference-ECG-period.

34. The method of claim 33, wherein an unbiased-averaged-difference-ECG-period is weighted by the periodicity values.

35. The method of claim 34, 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.