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

Presentation structure Results Methods Introduction Conclusions

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

Transvalvular blood flow patterns  MV signals: “M” shape  TV signals: Gauss shape E A

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

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

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

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

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

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 Low thresholdMethods background weak signal strong signal

Image enhancement

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

Edge processing Sobel NLLAP Sobel + NLLAP + Post processingMethods

Rough MVE extraction  MVE vector is extracted from the edge image: Using the biggest-gap algorithm a pixel is selected from each column Using the biggest-gap algorithm a pixel is selected from each columnMethods

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

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 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) Beat-by-beat comparison between the automatically extracted parameters and the manually extracted parameters (two technicians) Via Average-beat (manual vs calculated) Via Average-beat (manual vs calculated)Methods

Results  MV results  TV results Non-AF Non-AF AF AF

Results: Technicians vs. Automatic AFnon-AF MV: peak velocity MV: VTI TV : peak velocity AFnon-AF MV: peak velocity MV: VTI TV : peak velocity Automated Vs Technician 1 Automated Vs Technician 2AFnon-AF MV: peak velocity MV: VTI TV: peak velocity Automated Vs Technician avgAFnon-AF MV: peak velocity MV: VTI TV : peak velocity Technician 1 Vs Technician 2

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

Averaged Beat Experiments  Comparing the error between manual average and automated average to the error between manual average and representative beat Representative / Manual Automated / Manual Mean error 6.3%2.9% MV: peak velocity Non -AF 13.4%6.2% MV : VTI 9.7%4.9% TV : Peak Pressure 8.5%6.8% MV: peak velocity AF 13.0%4.6% MV : VTI 6.0%9.3% TV : Peak Pressure

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