Key result
A novel feature-based support vector machine (SVM) correctly classified 24 cases during testing, demonstrating effectiveness for automatically diagnosing left ventricular functionality.
Why the study?
Does a feature-based SVM algorithm accurately detect LV functionality abnormalities from echocardiogram sequences?
Does a feature-based SVM algorithm accurately detect LV functionality abnormalities from echocardiogram sequences?
A novel feature-based SVM algorithm demonstrates potential for automated detection of left ventricular structural changes and abnormalities from echocardiogram sequences.
Should not yet change LV assessment practice; leaves open SVM automation potential pending larger validation.
Detecting Left Ventricular (LV) structural changes from echo sequences can facilitate early Diagnosis and treatment of coronary artery disease. We develop a novel feature-based support vector machine (SVM) to detect the LV functionality as potential biomarkers. In the first phase, image pre-processing was carried out by using Shift 4 Algorithm. In the second phase, heart wall boundaries were detected by using Level set Algorithm. Then composite motion image creation was created using the heart wall boundaries, followed by feature extraction and statistical pattern recognition. In the third phase, dual LV contour region is segmented and segmented into LV individual segments. Volume, velocity of blood flow, and motion in between frames are calculated for each LV segment. The one against all SVM classifier was used to identify the LV segment abnormality. The performance of this approach was tested with 20 Abnormal and 20 Normal cases. We trained with 25 Normal & 25 Abnormal cases. During testing, we found that out of 24 cases are classified correctly. The results indicated that the SVM was an effective tool for automatically diagnosing LV functionality when compared to earlier works.
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G. Wiselin Jiji (2015) studied Left Ventricular structural changes / coronary artery disease (n=90). Feature-based support vector machine (SVM) was evaluated on Detection of LV segment abnormality. A novel feature-based support vector machine (SVM) correctly classified 24 cases during testing, demonstrating effectiveness for automatically diagnosing left ventricular functionality.
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