Download Free Medical Heart Sound Analysis: Theory Techniques and Applications PowerPoint Presentation

Heart Sound Analysis: Theory Techniques and Applications PowerPoint Presentation

DOWNLOAD MEDICAL POWERPOINT PRESENTATION

About This Presentation

Description : Check out this medical presentation on Heart Murmur, which is titled "Heart Sound Analysis: Theory, Techniques and Applications", to know about the basic anatomy and physiology of the heart, cardiac measurements and diagnosis, origin and characteristics of heart sounds, techniques for heart sound analysis, and applications of heart sound analysis.

Tags : Heart Murmur, Heart Sound Analysis, Theory, Techniques, Applications, basic anatomy, physiology

Published on : Jul 01, 2021 | Views : 1842 | Downloads : 9

Shareable Url :

Embed Code :

Comment

Transcript

Slide 1-

Heart Sound Analysis: Theory, Techniques and Applications Guy Amit Advanced Research Seminar
Slide 2-

2 Outline Basic anatomy and physiology of the heart Cardiac measurements and diagnosis Origin and characteristics of heart sounds Techniques for heart sound analysis Applications of heart sound analysis
Slide 3-

3 Cardiovascular Anatomy
Slide 4-

4 The Electrical System
Slide 5-

5 The Mechanical System
Slide 6-

6 Modulating Systems The autonomous nervous system The hormonal system The respiratory system Mechanical factors Electrical factors
Slide 7-

7 Multi-System Interactions
Slide 8-

8 Multi-Signal Correlations Ventricular pressure Aortic pressure Atrial pressure Aortic blood flow Venous pulse Electrocardiogram Phonocardiogram Berne R.M., Levy M.N., Cardiovascular Physiology, 6th edition
Slide 9-

9 Heart Disease Heart failure Coronary artery disease Hypertension Cardiomyopathy Valve defects Arrhythmia
Slide 10-

10 Cardiac Measurements Volumes: Cardiac output CO=HR*SV Stroke volume SV=LVEDV-LVESV Ejection fraction EF=SV/LVEDV Venous return Pressures: Left ventricular end-diastolic pressure (preload) Aortic pressure (afterload) Time intervals: Pre-ejection period Left ventricular ejection time
Slide 11-

11 Cardiac Diagnosis Invasive Right heart catheterization (Swan-Ganz) Angiography Non-invasive Electrocardiography Echocardiography Impedance cardiography Auscultation & palpitation
Slide 12-

12 Heart Sounds S1 – onset of the ventricular contraction S2 – closure of the semilunar valves S3 – ventricular gallop S4 – atrial gallop Other – opening snap, ejection sound Murmurs
Slide 13-

13 The Origin of Heart Sounds Valvular theory Vibrations of the heart valves during their closure Cardiohemic theory Vibrations of the entire cardiohemic system: heart cavities, valves, blood Rushmer, R.F., Cardiovascular Dynamics, 4yh ed. W.B. Saunders, Philadelphia, 1976
Slide 14-

14 Audibility of Heart Sounds Rushmer, R.F., Cardiovascular Dynamics, 4yh ed. W.B. Saunders, Philadelphia, 1976
Slide 15-

15 Heart Sounds as Digital Signals Low frequency S1 has components in 10-140Hz bands S2 has components in 10-400Hz bands Low intensity Transient 50-100 ms Non-stationary Overlapping components Sensitive to the transducer’s properties and location
Slide 16-

16 Sub-Components of S1 Rushmer, R.F., Cardiovascular Dynamics Obaidat M.S., J. Med. Eng. Tech., 1993
Slide 17-

17 Sub-Components of S2 Obaidat M.S., J. Med. Eng. Tech., 1993
Slide 18-

18 Heart Sound Analysis Techniques R.M. Rangayyan, Biomedical Signal Analysis, 2002
Slide 19-

19 Segmentation External references (ECG, CP) Timing relationship Spectral tracking Envelogram Matching pursuit Adaptive filtering
Slide 20-

20 Decomposition (1) Non-parametric time-frequency methods: Linear Short-Time Fourier Transform (STTF) Continuous Wavelet Transform (CWT) Quadratic TFR Wigner-Ville Distribution (WVD) Choi-Williams Distribution (CWD)
Slide 21-

21 Decomposition (2) Parametric time-frequency methods: Autoregressive (AR) Autoregressive Moving Average (ARMA) Adaptive spectrum analysis
Slide 22-

22 Decomposition - Example Bentley P.M. et al., IEEE Tran. BioMed. Eng., 1998 WVD CWD STFT CWT
Slide 23-

23 Feature extraction Morphological features Dominant frequencies Bandwidth of dominant frequencies (at -3dB) Integrated mean area above -20dB Intensity ration of S1/S2 Time between S1 and S2 dominant frequencies AR coefficients DWT-based features
Slide 24-

24 Classification Methods: Gaussian-Bayes K-Nearest-Neighbor Artificial Neural-Network Hidden Markov Model Rule-based Classes: Normal/degenerated bioprosthetic valves Innocent/pathological murmur Normal/premature ventricular beat
Slide 25-

25 Classification - Example Durand L.G. et al., IEEE Tran. Biomed Eng., 1990
Slide 26-

26 Heart Sound Analysis Applications Estimation of pulmonary arterial pressure Estimation of left ventricular pressure Measurement & monitoring of cardiac time intervals Synchronization of cardiac devices
Slide 27-

27 Estimation of pulmonary artery pressure (Tranulis et al., 2002) Non-invasive method for PAP estimation and PHT diagnosis Feature-extraction using time-frequency representations of S2 Learning and estimation using a neural network Comparison to invasive measurement and Doppler-echo estimation Animal model
Slide 28-

28 Signal Processing Filtering the PCG signal: 100Hz high-pass filter 300Hz low-pass filter Segmentation of S2 by ECG reference Decomposition of S2 by TFR: Smoothed Pseudo-Wigner-Ville distribution Orthonormal wavelet transform
Slide 29-

29 Feature Extraction SPWVD features: Maximum instantaneous frequency of A2,P2 The splitting interval between A2 and P2 OWT features (for each scale): Maximum value The position of the maximum value The energy
Slide 30-

30 ANN Training and Testing A feed-forward, back-propagation ANN with one hidden layer The significance of the features and the size of the network were evaluated Training was conducted using 2/3 of the data using error-minimization procedure The NN estimations were averaged for series of beats and compared to the measured PAP
Slide 31-

31 Results A combination of TFR and OWT features gave the best results (r=0.89 SEE=6.0mmHg) The correct classification of PHT from the mean PAP estimate was 97% (sensitivity 100% ; specificity 93%)
Slide 32-

32 Estimation of left ventricular pressure PCG and pressure tracing are different manifestations of cardiac energy The PCG is proportional to the acceleration of the outer heart wall => proportional to the changes of intra-ventricular pressure S3 is an indication of high filling pressure or/and stiffening of the ventricular wall
Slide 33-

33 Amplitude of S1 and LV dP/dt Sakamoto T. et al., Circ. Res., 1965
Slide 34-

34 PCG as a Derivative of Pressure The transducer measures acceleration The acceleration is the second derivative of displacement/pressure Pressure can be estimated by integrating the PCG Heckman J.L., et al., Am. Heart J.,1982
Slide 35-

35 Measurement of cardiac time intervals
Slide 36-

36 Synchronization of cardiac assist devices Left ventricular assist device (LVAD) Intra-aortic balloon pump Implantable Cardioverter Defibrillator
Slide 37-

37 Summary Heart sounds/vibrations represent the mechanical activity of the cardiohemic system The heart sound signal can be digitally acquired and automatically analyzed Heart sound analysis can be applied to improve cardiac monitoring, diagnosis and therapeutic devices
Slide 38-

Thank You !
Slide 39-

39 Mathematical Appendix (1) STFT CWT WVD CWD
Slide 40-

40 Mathematical Appendix (2) AR ARMA Adaptive spectrogram
Slide 41-

41 Mathematical Appendix (3) SPWVD OWT