Hadeer  El-Saadawy, Manal  Tantawi, Howida A.  Shedeed, Mohamed  F. Tolba, 
    Heart Disease Diagnosis using Electrocardiogram (ECG) Signal Processing,
    Asian Journal of Information Technology,
    Volume 16,Issue 10,
    2017,
    Pages 771-782,
    ISSN 1682-3915,
    ajit.2017.771.782,
    (https://makhillpublications.co/view-article.php?doi=ajit.2017.771.782)
    Abstract: Heart beat classification is considered as the main tool for recognizing and diagnosing different heart
diseases. The automation of heart beat classification is very necessary due to the exhaustive process of the
24 h mentoring of Electro Cardio Gram (ECG) signal of the heart. Moreover, ECG is considered as one of the
most powerful tools for the diagnosis of heartbeats. In this study, a reliable automatic method is proposed
separately on lead 1 and lead 2 to discriminate 15 classes of heart beat mapped into five main categories keeping
into consideration the accuracy of each class besides the overall one. A dynamic segmentation strategy is
applied to consider the heart rate variation. Discrete Wavelet Transform (DWT) is applied to extract beat
features. Thereafter, the extracted features are subjected to Principle Component Analysis (PCA) to reduce the
features dimension. Two different classifiers (Support Vector Machine (SVM) and random forests) are then
applied on the reduced features to get the best results from the SVM classifier. Finally, the rejection method
is applied to fuse the results from both leads 1 and 2. Using MIT-BIH as a validation database, SVM classifier
achieved an overall accuracy of 99.5% and an average accuracy of 96.35% while random forests classifier
achieved the best overall accuracy of 99.99% but an average accuracy of 84.26%. The study introduced also
a comprehensive survey of recently researched work in the same application.
    Keywords: Electro Cardio Gram (ECG) signal;heart diseases diagnosis;heart beat classification;Discrete Wavelet Transform (DWT);Principle Component Analysis (PCA);Support Vector Machine (SVM);random forest