Detection of Mechanical Heart Valve Thrombosis Using Support Vector Machine

Abstract

Thrombosis on the valve that prevents the movement of mechanical heart valves is a fatal disease requiring urgent intervention. Thrombosis is detected by echocardiographic findings and/or CT images. In this study, it has been tried to determine the formation of thrombosis by listening method which has been used for controlling the functionality of the heart valves for years. For this, firstly heart sounds of patients with thrombosis and normal mechanical heart valves were recorded. Then, the first and second heart sounds (S1 and S2) were separated from the recorded sounds. After the frequency spectrum of S1 and S2 were found using autoregressive spectrum estimation methods, six features were obtained regarding the frequency components. Then, the features obtained are classified by support vector machine methods. The average accuracy is 95.18% as a result of running the classifier 500 times using 3-fold cross validation. The maximum accuracy value was found to be 100% by using the 3-fold cross validation.

Early Detection Of Falls In The Elderly Using Frequency Domain Features Of The Acceleration Signals

“…Düzleme ait fonksiyon bulunduktan sonra x k örneklerinin sınıflaması ise denklem (4)'e göre yapılır [24].…”