Oyku EravciNalan ǑzkurtEravci, OykuOzkurt, NalanT. Yildirim , R. Chbeir , L. Bellatreche , C. Badica2025-10-062023979835033890410.1109/INISTA59065.2023.103103282-s2.0-85179551291https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179551291&doi=10.1109%2FINISTA59065.2023.10310328&partnerID=40&md5=a8fcd4ba2c9bca548d5d359a3a13b73chttps://gcris.yasar.edu.tr/handle/123456789/8510https://doi.org/10.1109/INISTA59065.2023.10310328In this study we provide a deep learning approach for the classification of arrhythmias that uses a customized wavelet-based autoencoder (AE) model for feature extraction and an MLP model for beat classification. The main objective of this paper is to evaluate the performance of auto-encoder based deep learning algorithms and to automatically classify five types of cardiac arrhythmias such as normal heartbeat (NSR) right bundle branch block (RBBB) left bundle branch block (LBBB) and atrial premature beats (APC) premature ventricular beats (PVC). The proposed approach received average scores of 99.8% 99.8% and 99.7% 99.7% for accuracy precision recall and F1 on publicly available datasets. The outcomes of the experiments demonstrate the significance of using deep learning based models in the diagnosis of cardiac disease. © 2023 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessArrhythmia Classification, Autoencoder, Deep Learning, Discrete Wavelet Transformation, Deep Learning, Discrete Wavelet Transforms, Diseases, Learning Algorithms, Learning Systems, Arrhythmia Classification, Arrhythmia Detection, Auto Encoders, Beat Classification, Discrete Wavelets Transformations, Features Extraction, Learning Approach, Mlp Model, Performance, DiagnosisDeep learning, Discrete wavelet transforms, Diseases, Learning algorithms, Learning systems, Arrhythmia classification, Arrhythmia detection, Auto encoders, Beat classification, Discrete wavelets transformations, Features extraction, Learning approach, MLP model, Performance, DiagnosisArrhythmia ClassificationDeep LearningDiscrete Wavelet TransformationAutoencoderConference Object