1. Baseline Suppression in ECG-signals Lisette Harting

2. Contents Introduction to the problem –Problem approach ECG analysis Common used solutions & ideas Results Conclusions and recommendations Questions contents - introduction - approach - ECG - literature - results - conclusions

3. Introduction to the problem contents - introduction - approach - ECG - literature - results - conclusions

4. Function of the heart Distribute oxygen and nutrition contents - introduction - approach - ECG - literature - results - conclusions

5. Electrophysiology

6. Pathology Bad conductance of signal Second pacer also initiates contraction Needs to be destructed: destructor 2 types of operation: –Open chest –Minimal surgery (catheters, ablation) contents - introduction - approach - ECG - literature - results - conclusions

7. Measuring ECG/EG Where does the ECG origin? –Chest (only) resistive  potentials on the skin ~ potentials on heart * factor Three deductions of ECG contents - introduction - approach - ECG - literature - results - conclusions

8. Measuring ECG/EG Extremity leads –Einthoven –Goldberger contents - introduction - approach - ECG - literature - results - conclusions

9. Measuring ECG/EG Precordial leads contents - introduction - approach - ECG - literature - results - conclusions

10. Application Diagnostic system –Exercise ECG Operation room system contents - introduction - approach - ECG - literature - results - conclusions

11. Baseline drift In exercise ECG caused by –Movements of the patient –Breathing –Changing electrode – skin contact In operation room merely caused by –Breathing –Ablation contents - introduction - approach - ECG - literature - results - conclusions

12. Assignment Design of baseline drift filter for operation-room ECG –With test-signals for breathing originated baseline drift Later to be used in exercise ECG and other applications contents - introduction - approach - ECG - literature - results - conclusions

13. Specifications Input: –Multiple channels (6 to > 12) –Already first order high pass-filtered with cutoff frequency 0.5 Hz or 0.05 Hz contents - introduction - approach - ECG - literature - results - conclusions

14. Desired output: –Cutoff frequency 0.5 Hz –0.5 Hz and lower: minimal 6 dB attenuation –Delay maximal 120 ms –Minimize signal to noise ratio –Minimize distortion of signal –Must work real time on a normal computer contents - introduction - approach - ECG - literature - results - conclusions

15. Problem approach contents - introduction - approach - ECG - literature - results - conclusions

16. Work Literature study Oscillation filter on synthetic test signal IIR / FIR Analyzed experimental signals Made for-backward filter with heart rate adaptation Demonstration program contents - introduction - approach - ECG - literature - results - conclusions

17. To do: –Write report –Optimize chosen filter further –Work out theoretical problem No time for: –Adaptive filters contents - introduction - approach - ECG - literature - results - conclusions

18. ECG-signal analysis contents - introduction - approach - ECG - literature - results - conclusions

19. Time domain contents - introduction - approach - ECG - literature - results - conclusions

20. PSD contents - introduction - approach - ECG - literature - results - conclusions

21. SNR Signal to noise ratio (from PSD) –S/N = 10 * 10log(Ps/Pn) Signal –Heart rate and higher frequencies Noise –Rest of signal Compared qualities of the signals from the 19 experiments contents - introduction - approach - ECG - literature - results - conclusions

22. Heartrate Varied between 25 and 35 Was detected correctly 100% by the algorithm (to be discussed later) Not tested with ill patients contents - introduction - approach - ECG - literature - results - conclusions

23. Common solutions contents - introduction - approach - ECG - literature - results - conclusions

24. Filters Idea behind hp digital filters: –Out = In(delayed) – In(filtered) low pass filter delay + - contents - introduction - approach - ECG - literature - results - conclusions

25. Average based filters –Moving average filters (box) –Triangular FIR-filter With smart size of window to be able to use shifting instead of division after adding FIR –May be linear phase –But need large calculation power contents - introduction - approach - ECG - literature - results - conclusions

26. Bidirectional filters Input hardware filter is reversed in time and sampled Symmetric filter (zero phase shift) Problem: fixed cutoff frequency contents - introduction - approach - ECG - literature - results - conclusions

27. Incrementally changing filter Incrementally changing filter for QRS- complex and rest of ECG-signal contents - introduction - approach - ECG - literature - results - conclusions

28. Slew rate limiter slew rate limiter –Against fast increase of baseline drift (optimize step response) –Limit rising and falling rate of the signal contents - introduction - approach - ECG - literature - results - conclusions

29. Other solutions contents - introduction - approach - ECG - literature - results - conclusions

30. Heart rate detection Simple algorithm: –Derivative < minimal value  count+1 –Derivative > minimal value  reset count –If count > limit  QRS-complex detected reset count pause detection algorithm 100 ms adjust cutoff frequency filter –Time between 2 complexes = heart rate contents - introduction - approach - ECG - literature - results - conclusions

31. Envelope method Baseline drift estimation: –envelope around input signal –Estimation is mean of the envelope Idea: –Use information about ECG phase –to correct for distortion of ECG –based on (measured) phase dependent distortion of a pure ECG-signal contents - introduction - approach - ECG - literature - results - conclusions

32. Adaptive oscillator Principle: –Suppress ECG-signal (SLR or lp-filter) –After SLR-interval: average is BLD- estimation –Use 2 BLD-estimates to predict 3 rd (IIR): d(n) = a(n) * d(n-w) – d(n-2w) –Update ‘a’ a(n+1) = a(n) + [d_real(n) – d_meas(n)] / d(n-1) contents - introduction - approach - ECG - literature - results - conclusions

33. Cross-Correlation filter Principle –Do not adapt filters one by one, but use knowledge about other signals Why? –There is a high correlation between the signals contents - introduction - approach - ECG - literature - results - conclusions

34. Cross-correlation filter Why not? –Fast (10 s) and high (90%) changes of the correlation between the signals –Low frequencies need a lot of time & memory to calculate correlation –Non-linear relation between signals –Heart rate would need to be filtered out too contents - introduction - approach - ECG - literature - results - conclusions

35. For-backward filtering Principle –Minimize calculation time decimation IIR-filtering –Linearize and increase steepness IIR-filter by filtering also backward contents - introduction - approach - ECG - literature - results - conclusions

36. For-backward filtering Working: –Prefilter signal with cutoff freq. 10 Hz. –Decimate signal with 50 to 40 Hz. –Filter signal again with cutoff freq. 0.5 Hz. –Interpolate signal –Filter out high frequency components introduced by interpolated signals contents - introduction - approach - ECG - literature - results - conclusions

37. For-backward filtering Filtering: –IIR –Continuously forward –Backward over window window > max. delay filter for all frequencies last filtered sample is filtered value –Apply together with heart rate adaptation contents - introduction - approach - ECG - literature - results - conclusions

38. Which can be tried Adaptive oscillator FIR IIR For-backward filter + Heart rate adaptive filter Envelope (but no time) contents - introduction - approach - ECG - literature - results - conclusions

39. Results contents - introduction - approach - ECG - literature - results - conclusions

40. Adaptive oscillator The adaptive oscillator was not stable Step-adaptation of parameters -in order to stabilize- deformed the shape of the ECG- signal Because of fast changes of sinusoid  unstable Non-linear Does not work when other noise is present contents - introduction - approach - ECG - literature - results - conclusions

41. FIR Principle –The ideal response of an analogue filter is truncated –Length: half (180 degrees) cutoff frequency 0.5 Hz: 1 sec; 0.05 Hz: 10 sec. It is the standard solution But delay >= 1 second contents - introduction - approach - ECG - literature - results - conclusions

42. Moving average (2000 points) contents - introduction - approach - ECG - literature - results - conclusions

43. Chebyshev (1000-points; 10dB sidelobe-supression) contents - introduction - approach - ECG - literature - results - conclusions

44. IIR –Fast (minimal one sample) But –phase shift causes Distortion of ECG-signal The same delay of the signal contents - introduction - approach - ECG - literature - results - conclusions

45. Time-domain contents - introduction - approach - ECG - literature - results - conclusions

47. Frequency domain contents - introduction - approach - ECG - literature - results - conclusions

49. Filters Prefilter before decimation contents - introduction - approach - ECG - literature - results - conclusions

50. Heart rate filter contents - introduction - approach - ECG - literature - results - conclusions

51. SNR SNR-improvement is quite high But for signals with little noise, the SNR improvement can be negative

52. Distorsion contents - introduction - approach - ECG - literature - results - conclusions

53. Step response contents - introduction - approach - ECG - literature - results - conclusions

54. Calculation power Depend on window width (‘win’) decimation factor (‘dec’) Decimation filter forward:1 backward:1 Filter forward: 1/dec backward:win/dec Interpolationforward1 backwarddec TOTAL = 3 + (win + dec + 1)/dec contents - introduction - approach - ECG - literature - results - conclusions

55. Delay (samples) Decimation filter forward:1 backward:dec Filter forward: dec backward:dec*win Zero order interpolation0.5 * dec Interpolationforward1 backwarddec TOTAL = 2 + (win + 3.5)*dec (samples) contents - introduction - approach - ECG - literature - results - conclusions

56. TOTAL = 2 + (win + 3.5)*dec (samples) Optimal: –win = 13 (minimal) –dec = 40 This makes total: –(2 + 16.5*40) / 2000 = 662 / 2000 = 0.331 sec. contents - introduction - approach - ECG - literature - results - conclusions

57. Demonstration..\Program contents - introduction - approach - ECG - literature - results - conclusions

58. Demonstration contents - introduction - approach - ECG - literature - results - conclusions

59. Conclusions and recommendations contents - introduction - approach - ECG - literature - results - conclusions

60. Conclusions Heart rhythm adaptation works good, in this case Heart rate filter is working, but needs to be improved contents - introduction - approach - ECG - literature - results - conclusions

61. Recommendations The heart-rate filter should be tested on more data: Does the simple heart rate detection system work –in all situations? –on all patients? It needs to be improved for e.g. systoles en PVC’s contents - introduction - approach - ECG - literature - results - conclusions

62. Automatically calculate minimal window- width Introduce 2 delay-modes Optimize parameters Quantize distortion Use only analogue low-pass filter Try other bidirectional filters (same as our filter) contents - introduction - approach - ECG - literature - results - conclusions

63. Questions!