Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 2019, Faculty of Sciences, Ibn Tofaïl University -Kénitra- Morocco

Research Article

A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database

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  • @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
RACHID HADDADI1,*, Elhassane Abdelmounim1, Mustapha El Hanine1, Abdelaziz Belaguid2
  • 1: Laboratory of Systems Analysis and Information Processing, Faculty of Sciences and Technics, Hassan first University, Settat, Morocco.
  • 2: Laboratory of physiology, Mohammed-V University Rabat, Morocco
*Contact email: haddadirachid@hotmail.com

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.