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Automated Method for Doppler Echocardiography Analysis in Patients with Atrial Fibrillation

Automated Method for Doppler Echocardiography Analysis in Patients with Atrial Fibrillation. O. Shechner H. Greenspan M. Scheinowitz The Department of Biomedical Engineering and M.S. Feinberg The Heart institute, Sheba Medical Center, Tel Hashomer Tel Aviv University, Tel Aviv, Israel.

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Automated Method for Doppler Echocardiography Analysis in Patients with Atrial Fibrillation

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  1. Automated Method for Doppler Echocardiography Analysis in Patients with Atrial Fibrillation O. Shechner H. Greenspan M. Scheinowitz The Department of Biomedical Engineering and M.S. Feinberg The Heart institute, Sheba Medical Center, Tel Hashomer Tel Aviv University, Tel Aviv, Israel

  2. Presentation structure Introduction Methods Results Conclusions

  3. Introduction • Doppler echocardiography: • Non invasive modality for the assessment of cardiac function • Blood flow velocity tracing through the heart valves can be obtained by transthoracic Doppler echocardiography. • Extracted data: • Maximal Velocity Envelope (MVE) • Peak velocity • Peak and mean pressure • Velocity-time integral (VTI)

  4. Transvalvular blood flow patterns • MV signals: “M” shape • TV signals: Gauss shape E A

  5. MV signals: only E-wave present due to the loss of atrial contraction TV signals: inter-beat amplitude variability Atrial Fibrillation • Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia • AF characterized by irregular heart rate, electrogram and haemodynamic changes. E E E E E

  6. Manual methods • Time consuming • Inter and intra observer variability • Difficulties when dealing with AF patients • Doppler image analysis • MVE estimation by averaging points and fitting into a kinetic model (Hall et al, 1995-1998) • Edge detection-based algorithm for Brachial artery Doppler tracings (Tschirren et al, 2000) • Validation using phantoms, simulations and normal patient groups Early work

  7. Our work • Automated analysis of MV and TV Doppler signals • Validation on a large dataset of both AF and non-AF patients

  8. Parameter curve fitting Parameter extraction Point linking Proposed Framework Input Image Image separation into ECG and Signals Signal enhancement Signal processing: Edge detection ECG analysis: segmentation into cardiac cycles Rough MVE extraction Parameters

  9. Methods Image separation • Dividing the image into region of interest (ROI) and ECG signal: • The ECG signal is extracted by its color • The location of the horizontal axis is found using horizontal projection – ROI extraction ROI Original Image ECG

  10. Methods Image enhancement • Segmentation of ROI pixels by their gray level into three clusters (K-means) • Contrast stretching improves image contrast and suppresses noise High threshold background weak signal strong signal Low threshold

  11. Image enhancement

  12. Methods Signal processing: Edge detection • Combining the Sobel operator with the non-linear Laplace operator (NLLAP): d(x,,y) – Neighborhood of (x,y) • NLLAP introduces adaptive orientation of the Laplace operator • Edge is detected at places of zero crossings • Thresholding is applied on the edge strength

  13. Methods Edge processing NLLAP Sobel Sobel + NLLAP + Post processing

  14. Methods Rough MVE extraction • MVE vector is extracted from the edge image: • Using the biggest-gap algorithm a pixel is selected from each column

  15. Methods Linking Anchor point • The linking process is done beat-wise • maximal vertical value taken as anchor • Ascending and descending slopes are detected • Vertical “Noise level” is determined • Starting slopes are determined; slopes are interpolated from starting slope to anchor point “noise level”

  16. Methods Parameter fitting • The MVE is fitted into a parameter model using the Levenberg-Marquardt algorithm (MSE criteria) • Partial Fourier series model is used (TV: n=4; MV: n=5) • Parameter extraction

  17. Methods Experimental Setup • Dataset: 467 beats from 121 images that were taken from 45 patients (25 AF, 20 non-AF) • Validation: • Beat-by-beat comparison between the automatically extracted parameters and the manually extracted parameters (two technicians) • Via Average-beat (manual vs calculated)

  18. MV results TV results Results Non-AF Non-AF AF AF

  19. Results: Technicians vs. Automatic Automated Vs Technician 2 Automated Vs Technician avg Automated Vs Technician 1 Technician 1 Vs Technician 2

  20. Results: Technicians vs. Automatic (cont.) Peak velocity TV signals MV signals y = 0.95x + 0.097 y = 1.02x + 5.50 AF y = 1.12x + 7.75 y = 1.16x + 0.39 non-AF

  21. Averaged Beat Experiments • Comparing the error between manual average and automated average to the error between manual average and representative beat

  22. Conclusions • The possibility of automated system for MV/TV Doppler image analysis was shown • The system is robust and manages to deal with both AF and non-AF signals with different morphology • Parameters are extracted from all the beats in the image, allowing the computation of an accurate average

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