Study on Ventricular Fibrillation by Using Wavelet and Identification with SVM

Abstract

Digital signal processing and data analysis are very often used methods in biomedical engineering research. Automated external defibrillators (AEDs) are portable electronic devices that automatically diagnose the potentially life-threatening cardiac arrhythmias of ventricular fibrillation in a patient. However, the precise identification of electrocardiogram waveforms is difficult in the present AEDs. The identification of state transition in the waveforms has never been dealt with. Heart signals represent an important way to evaluate cardiovascular function and often what is desired is to quantify the level of some signal of interest against the louder backdrop of the beating of the heart itself. An example of this type of application is the quantification of cavitation in mechanical heart valve patients. The aim of this project is to propose a method for improving the identification accuracy of electrocardiogram waveforms including the state transition. This will be achieved by using the wavelet transforms and the support vector machine.