How To Segmentate Heart Sound

Transcription

1 Heart Sound Segmentation: A Stationary Wavelet Transform Based Approach Author: Nuno Marques Advisors: Rute Almeida Miguel Coimbra

2 Classifying Heart Sounds PASCAL Challenge The challenge had 2 tasks: Segmentation and Classification and Anomaly Detection This work describes what i did in the first task: Segmentation and Classification

3 44100 Hz 20 auscultations istethoscope Datasets Digiscope 4000 Hz 80 auscultations Non-controlled environment No expert! Who was auscultated? Controlled environement Done by expert! Auscultation were performed on infants exclusively!

4 Heart Sounds We want to detect and distinguish these two peaks! (which are the heart sounds!)

5 How do you detect and distinguish heart sounds?

6 Heart Sound Segmentation Cardiac Segmentation algorithms can be successfully divided in 4 phases: Preprocessing Segmentation Representation Classification

7 P Preprocessing

8 P Preprocessing

9 R Préprocessamento Representation

10 R Préprocessamento Representation Segmentação

11 Préprocessamento Segmentation Segmentação S

12 Préprocessamento Segmentation Segmentação S

13 S Préprocessamento Segmentation

14 S Préprocessamento Segmentation

15 Préprocessamento Classification Segmentação S1/S2/sistole/diastole?! C

16 Préprocessamento Classification Segmentação S1 sistole S2 diastole S1 C

17 P Manual Annotation

18 P Manual Annotation

19 Fourier Transform P Mediana 1 Mediana 2...

20 P Spectral Analysis

21 Pre-Processing Just downsampled istethoscope! P

22 R Representation A good cardiac signal representation should have 2 characteristics g 1 e g 2

23 R g 1. Accentuate the difference between S1/S2 and sistole/diastole sistole diastole S1 S2

24 R g 2. Accentuate the difference between S1 and S2 S1 S2

25 Representation Shannon Energy Envelope Shannon Entropy Envelope R Domínio do tempo

26 R Shannon Energy Envelope

27 R Shannon Energy/Entropy

28 Representations Continuous Wavelet Transform Discrete Wavelet Transform Stationary Wavelet Transform S-Transform Empirical Mode Decomposition Hilbert-Huang Transform R Time-Frequency Domain

29 R Digiscope Results

30 R istethoscope Results

31 Segmentation We can divide the Segmentation phase into 2 sub-phases: Peak Detection Boundary Detection S

32 S Peak Detection

33 S Boundary Detection

34 S Convolution

35 Idea! Use a filter in the SWT that looks like the S1/S2 in order to determine their boundaries! S

36 Stationary Wavelet Transform g 3 [n] Scale 3 Coeffs g 1 [n] g 2 [n] h 2 [n] h 3 [n] Scale 2 Coeffs x[n] h 1 [n] Scale 1 Coeffs g j [n] 2 g j+1 [n] Daubechies 38 h j [n] 2 h j+1 [n] S

37 Problem g 3 [n] Scale 3 Coeffs g 2 [n] h 3 [n] g 1 [n] h 2 [n] Scale 2 Coeffs x[n] g j [n] h j [n] 2 2 h 1 [n] g j+1 [n] h j+1 [n] Scale 1 Coeffs Stationary Wavelet Transform S

38 Problema... x[n] g 1 [n] g 9 [n] ( ) h 10 [n] S Signal becomes completely deformed!

39 Solution Lets use the Convolution s Associative property!... x[n] g 1 [n] g 9 [n] h 10 [n] ( ) =... x[n] g 1 [n] g 9 [n] h 10 [n] ( ) S

40 Solution x[n]... g 1 [n] g 9 [n] h 10 [n] ( ) S

41 Signal Transformation: Digiscope x[n] Shannon energy (x[n]) S

42 Signal Transformation: istethoscope x[n] Shannon entropy (x[n]) S

43 S Shannon Energy

44 S Wavelet Coefficients

45 S Inflection Points

46 Segment Descriptors - Maximum S

47 Segment Descriptors - S1/S2 - Sistole/Diastole S

48 S

49 S PASCAL Challenge Results

50 S Determining Boundaries

51 Determining Boundaries Variation between Segments Longest Increasing/Decreasing Sub-sequence S

52 Variation Between Segments(a 1 ) Maximum length of segment Minimum length Of segment S

53 Longest Increasing/Decreasing Sub-sequence(a 2 ) Longest Increasing Sub-Sequence Longest Decreasing Sub-Sequence S

54 Baseline Method(a 3 ) Maior sub-sequência crescente Maior sub-sequência decrescente S

55 Results Média +- desvio padrão (ms) S

56 Classification

57 Préprocessamento Classification Segmentação S1/S2/sistole/diastole?!

58 Individual descriptor - Máximo This segment s descriptor C

59 Expanded Descriptor - Máximo This segment s descriptor C

60 Combination of descriptors: - Máximo Individual This segment s descriptor C

61 Combination of descriptors: - Máximo Expanded This segment s descriptor C

62 Results: Combination of Descriptors C

63 Conclusion

64 Conclusion Spectral Analysis Evaluation of different types of Representations New peak detection algorithm 2 new boundary detection algorithms Article publication in Computing in Cardiology 2013

65 Thank you!