Date of Award


Document Type


Degree Name

Master of Science (MS)


Biomedical and Chemical Engineering and Sciences

First Advisor

Mehmet Kaya

Second Advisor

Ersoy Subasi

Third Advisor

Linxia Gu

Fourth Advisor

Daniel Kirk


Cardiac diseases are the most common cause of mortality in the world. The detection of cardiac arrhythmias is not a straightforward process, since minor variations in the electrocardiogram (ECG) signals cannot be easily identified manually. Therefore, automatic detection and classification of cardiac arrhythmia would shorten the diagnostic time and accelerate medical intervention resulting in reducing the mortality rate. In this thesis, I have developed a simple and low-cost computer-aided diagnostic system using MATLAB-based Graphical User Interface (GUI) to facilitate fast operation and access to the data along with the overall accuracy of the system. The acquired ECG signals are processed by wavelet-based filtering and feature extraction techniques using Daubechies (db) wavelets to determine a combination of 15 statistical features. The significant wavelet features were subsequently used as categorical inputs to perform pattern recognition of the ECG signals using artificial neural network (ANN), support vector machine (SVM), and random forest (RF) and classify the output into normal or abnormal classes. The performance of the proposed model was evaluated using Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database (MIT-BIH AD) over 46 ECG records including normal and arrhythmias signals. The overall system performance was achieved with 98.3%, 95.65%, and 100% overall accuracy using ANN, SVM, and RF, respectively.


Copyright held by author