Research Article
A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database
@INPROCEEDINGS{10.4108/eai.24-4-2019.2284216, author={RACHID HADDADI and Elhassane Abdelmounim and Mustapha El Hanine and Abdelaziz Belaguid}, title={A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database }, proceedings={Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 2019, Faculty of Sciences, Ibn Tofa\~{n}l University -K\^{e}nitra- Morocco}, publisher={EAI}, proceedings_a={ICCWCS}, year={2019}, month={5}, keywords={electrocardiogram dwt qrs complex convolutional neural network}, doi={10.4108/eai.24-4-2019.2284216} }- RACHID HADDADI
Elhassane Abdelmounim
Mustapha El Hanine
Abdelaziz Belaguid
Year: 2019
A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database
ICCWCS
EAI
DOI: 10.4108/eai.24-4-2019.2284216
Abstract
In this work, we present an ECG delineation and the automated diagnosis of coronary artery disease in the electrocardiogram (ECG). In preprocessing stage, the baseline wander (BLW) and 60 Hz power line interference (PLI) were removed using discrete wavelet transform (DWT). The QRS detection is carried out using Daubechies (Db4) DWT. Feature extraction and classification is done using a convolutional neural network (CNN) containing three convolutional layers, three max-pooling layers, and three fully connected layers. The standard 12 lead ECG signals of 50 healthy subjects and 50 myocardial infarction subjects (MI) of one minute are obtained from the Physikalisch-Technische Bundesanstalt (PTB) database. We achieved an accuracy of 94.83%. sensitivity of 94.75%, and specificity of 94.93% on PTB database.
