IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 10, No. 4, December 2021, pp. 960~970 ISSN: 2252-8938, DOI: 10.11591/ijai.v10.i4.pp960-970  960 Journal homepage: http://ijai.iaescore.com Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier Youssef Toulni 1 , Taoufiq Belhoussine Drissi 2 , Benayad Nsiri 3 1,2 Laboratory Industrial Engineering, Information Processing, and Logistics (GITIL), Faculty of Science Ain Chock, University Hassan II-Casablanca, Morocco 1,3 Research Center STIS, M2CS, National School of Arts and Crafts of Rabat (ENSAM), Mohammed V University in Rabat, Morocco Article Info ABSTRACT Article history: Received Nov 29, 2020 Revised Sep 10, 2021 Accepted Sep 28, 2021 The electrocardiography allowed us to make a diagnosis of several cardiovascular diseases by representing the electrical activity of the heart over time; this representation is called the electrocardiogram (ECG) signal. In this study we have proposed a model based on the processing of the ECG signal by the wavelet decomposition using discrete wavelet transform (DWT). This decomposition firstly makes it possible to denoise the signal then to extract the statistical features from the approximation coefficients of the denoised signal and finally to classify the data obtained in a support vector machine (SVM) classifier with cross validation for more credibility. After having tested this model with different mother wavelets at different scales, the accuracies at the fourth scale are high and the best accuracy obtained is 87.50%. Keywords: Cardiovascular disease Discrete wavelet transform Electrocardiogram signal Support vector machine This is an open access article under the CC BY-SA license. Corresponding Author: Youssef Toulni Laboratory Industrial Engineering, Information Processing, and Logistics (GITIL) Faculty of Science Ain Chock Km 8 Route d'El Jadida, B.P 5366 Maarif Casablanca 20100 Morocco Email: youssef.toulni@gmail.com 1. INTRODUCTION Cardiovascular disease is a collection of irregularities affecting the heart; it is considered one of the most important causes of death in the world. According to the World Health Organization, there were approximately 17.9 million deaths in 2016; this big number represents 31% of deaths worldwide [1]. The lives of people with cardiovascular diseases are in constant danger, quick and effective diagnosis of these diseases can save a lot of lives. Several techniques in the medical field are used to diagnose cardiovascular disease, such as blood tests, coronary angiography, cardiac MRI, X-ray and electrocardiography. However, most of these techniques require medical assistance from experienced people, which is not always the case if we knew that almost 30% of cases with these diseases come from poor countries. Electrocardiography is a non-invasive detection technique based on recording the electrical activity of the heart over time [2], the signal obtained during this recording is called an electrocardiogram (ECG). The ECG signal is considered among the most widely used biomedical signals to detect heart problems, ECG signal contains a large number of information that can be of great interest in the detection and diagnosis of many heart diseases [3] which appears in some distortions of the signal shape. Despite the advantages of the use of ECG signals, there are many limitations of this technique; the difficulty of interpreting the signal and the lack of experienced personnel are among the constraints most encountered during identification of ECG signals; also, ECG signal contains various unwanted noises that prevent the correct extraction of useful and necessary