College - Author 1

College of Engineering

Department - Author 1

Electrical Engineering Department

Degree Name - Author 1

BS in Electrical Engineering

College - Author 2

College of Engineering

Department - Author 2

Electrical Engineering Department

Degree - Author 2

BS in Electrical Engineering



Primary Advisor

Xiao Hua Yu, College of Engineering, Electrical Engineering Department


Cardiovascular diseases (CVDs) are the highest leading cause of death worldwide with an approximate 17.9 million related deaths every year according to the World Health Organization (WHO). Electrocardiographic (EKG or ECG) signals are electrical signals measured in the heart and are the main indicator for pre-existing cardiac conditions. The application of deep learning methods such as artificial neural networks (ANN) will assist in the automated detection and classification of ECG signals. The current methods for ECG analysis are lacking in accuracy and reliability considering the level of risk involved with CVDs and the importance of a correct diagnosis. Clinicians use ECG data to find irregular patterns that may indicate the existence of potentially life-threatening cardiac conditions. The proposed method uses the discrete wavelet transform for feature extraction and a feed-forward neural network for the classification of six types of heartbeats. This approach achieves impressive results in terms of accuracy, sensitivity, and specificity across all classes. The best results obtained were 97.87%, 99.57%, and 97.87%, respectively. Although simpler than other state-of-the-art methods, this approach achieves competitive results and can help reduce the chance of misdiagnosis and missed diagnosis.

Available for download on Wednesday, June 10, 2026