A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity

Riyanto Sigit (1), Achmad Basuki (2), - Anwar (3)
(1) Politeknik Elektronika Negeri Surabaya
(2) Politeknik Elektronika Negeri Surabaya
(3) Politeknik Elektronika Negeri Surabaya
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How to cite (IJASEIT) :
Sigit, Riyanto, et al. “A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 3, June 2020, pp. 964-73, doi:10.18517/ijaseit.10.3.12152.
Echocardiography is a method of examination with high-frequency sound waves to obtain images of heart organs. Examination of heart health conditions with echocardiography as an imaging method, serves to detect the potential for heart disease, thus that the right treatment from the evaluation results can be decided. Examination of the source of heart disease with echocardiography was performed using several views, namely the long axis, short axis, two-chamber, and four-chamber. However, the assessment of cardiac function is still carried out conventionally. Thus it is necessary to build a system that can assess cardiac function. This study proposes a feature extraction method for the classification of heart disease based on the left ventricular motion on the short-axis. In this method, feature extraction uses 24 good features for the process of tracking the movement of the left ventricle with optical flow. Each good feature produces four features, namely direction (negative direction and positive direction) and distance (negative distance and positive distance) from the results of left ventricular tracking and produces 96 attributes for the whole process. The features that have been obtained are then processed using several classification algorithms with validation techniques that are, k-folds, and leave one out. The result is a classification algorithm with a gradient boosting classifier method that has the best accuracy. Gradient boosting classifier produces accuracy values with validation techniques for k-folds 90.98%, and leave one out 93.23%. This shows that the gradient boosting classifier can be relied upon for the classification of heart disease using the proposed feature extraction method. In this study, we developed a new feature extraction method from the results of tracking the heart wall using optical flow. This algorithm can produce feature values from the tracking results that can be used to build a knowledge system for the classification of heart health conditions.

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