Senturk, U.Polat, K.Yucedag, I.Alenezi, F.2024-08-232024-08-232023978-032396129-5978-032399681-5https://doi.org/10.1016/B978-0-323-96129-5.00002-0https://hdl.handle.net/20.500.12684/14764A noninvasive technique, electrocardiography (ECG), is crucial in the detection and treatment of cardiovascular disorders. Periodic beats make up ECG signals, and these beats vary based on the internal dynamics of the cardiovascular system. It is highly challenging to categorize ECG beats that can be understood by specialists in the area. In recent years, attempts have been made to use artificial intelligence programs in conjunction with a database of specified ECG beats to identify autonomous cardiovascular illness. In this investigation, the PTB Diagnostic ECG Database and the MIT-BIH Arrhythmia Database were used to attempt to classify arrhythmias. A one-dimensional convolutional neural network (CNN, or ConvNet) model was used to estimate the arrhythmia classes. The ECG beats defined in the database are divided into five classes: normal (N), supraventricular premature (S), premature ventricular contraction (V), ventricular and normal fusion (F), and Unclassifiable beats (U). The utilized one-dimensional convolutional neural network (1D-CNN VGG16) model’s average accuracy in classifying arrhythmias was found to be 99.12%. With the aid of this study, a system of experts has been built to assist specialized doctors in the healthcare system. The high estimating success of the used model will help in combining the right diagnosis with the right therapy and saving lives. © 2023 Elsevier Inc. All rights reserved.en10.1016/B978-0-323-96129-5.00002-0info:eu-repo/semantics/closedAccessArrhythmia classificationcardiovascular diseaseECG signal beatsone dimension CNNtime series CNNBook Chapter831012-s2.0-85161110068N/A