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    Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks
    (Elsevier, 2023) Senturk, U.; Polat, K.; Yucedag, I.; Alenezi, F.
    A 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.
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    Classification of pressure ulcer images with logistic regression
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yilmaz, B.; Atagun, E.; Demircan, F. O.; Yucedag, I.
    Pressure ulcers are wounds caused by prolonged lying in bedridden patients. This has become an important health problem in many countries. Correct diagnosis of pressure ulcers is very important for an effective treatment method. The characteristics of these wounds are effective in terms of seeing the healing. Interventional methods of obtaining information in the diagnosis of pressure ulcers are painful for patients. In addition, these methods can increase the risk of infection. Therefore, imaging systems such as nonsurgical wound tracking techniques allow accurate analysis of the features of the wound without contact with it. The aim of this study is to prevent wound formation or to make a positive contribution to the treatment processes by using machine learning techniques in image analysis for the classification of pressure ulcers. In this study, 142 wound images were analyzed by Logistic Regression and Artificial Neural Networks methods. Features such as wound color and size in these images were separated by image processing and the stage of the wound was determined from the images. The 6 stages of pressure ulcers are referenced for classification. © 2021 IEEE.