An electrocardiogram contains a lot of information about heart function, which we can use to predict and detect different types of heart diseases. The main step of automatic analysis of ECG sygnals is to find QRS complexes that are used to detect heart beats.
In this master thesis we will learn about different wavelets and wavelet transforms. Moreover, we will present a method for QRS detection based on the use of wavelets and discrete wavelet transform. We will also describe a discrete wavelet transform based algorithm for a noise reduction. The detection method will be evaluated on two databases: MIT-BIH arrhythmia database and CU ventricular tachyarrhythmia database. In addition, we will compare the performance of the methods using three types of wavelets: Daubechies wavelets, coiflets and symlets. Finally, we will qualitatively evaluate the method on selected ECG recordings from the MIT-BIH database that contain certain features.
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