Çağla SarvanNalan ǑzkurtSarvan, CaglaOzkurt, Nalan2025-10-062019978172812420910.1109/TIPTEKNO.2019.88950142-s2.0-85075625815https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075625815&doi=10.1109%2FTIPTEKNO.2019.8895014&partnerID=40&md5=c8e48dde957fe033393234376374223chttps://gcris.yasar.edu.tr/handle/123456789/9368https://doi.org/10.1109/TIPTEKNO.2019.8895014https://doi.org/10.1109/tiptekno.2019.8895014In this study ECG arrhythmia types of non-ectopic (N) ventricular ectopic (V) unknown (Q) supraventricular ectopic (S) and fusion (F) were classified by using the convolutional neural network (CNN) architecture. QRS detection was performed on these ECG arrhythmias that downloaded from MIT-BIH database. An imbalanced number of beats was obtained for 5 different arrhythmia types. In order to reduce the effect of imbalance in statistical performance metrics data mining techniques such as recall of data were applied. It was aimed to increase the positive predictive value (PPV) rates of the classes which consist of a few instances. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessArrhythmia, Cnn, Data Mining, Ecg, Imbalanced, Biomedical Engineering, Diseases, Electrocardiography, Neural Networks, Arrhythmia, Arrhythmia Classification, Class Imbalance, Convolutional Neural Network, Imbalanced, Mit-bih Database, Positive Predictive Values, Statistical Performance, Data MiningBiomedical engineering, Diseases, Electrocardiography, Neural networks, Arrhythmia, Arrhythmia classification, Class imbalance, Convolutional neural network, Imbalanced, MIT-BIH database, Positive predictive values, Statistical performance, Data miningECGCNNImbalancedArrhythmiaData MiningConference Object