Yazar "Erdemir, Fatih" seçeneğine göre listele

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    A Decision Support System on Artificial Intelligence Based Early Diagnosis of Sepsis
    (2022) Kaya Aksoy, Pınar; Erdemir, Fatih; Kılınç, Deniz; Er, Orhan
    Sepsis is the intense reaction of the immune system as a result of a severe infection in any part of the body and damages to organs and tissues. And this disease is commonly fatal and costly. In this study, we perform a comparative study for Sepsis prediction using machine learning algorithms from original laboratory findings. For this purpose, thirty-two different machine learning algorithms including different tructures as well as neural network classifiers are evaluated and compared. As a result of experimental studies, SVM (Cubic, Fine Gaussian), KNN (Fine, Weighted, Subspace), Trees (Weighted, Boosted, Bagged) and neural network-based classifiers have achieved a significant success rate in the diagnosis of Sepsis using the new dataset. Thus, it is concluded that it is appropriate to use machine learning algorithms to predict whether a Sepsis patient will be survived. This study has the potential to be used as a new supportive tool for doctors when predicting Sepsis.
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    Classification of electrocardiogram (ECG) data using deep learning methods
    (Institute of Electrical and Electronics Engineers Inc., 2020) Bozyiğit, Fatma; Erdemir, Fatih; Şahin, Murat; Kılınç, Deniz
    Classification is one of the most widely used techniques in healthcare, especially concerning diagnosing cardiac disorders. Arrhythmia is a disorder of the heartbeat rate or rhythm, which may occur sporadically in daily life. Electrocardiogram (ECG) is an important diagnostic tool for analysing cardiac tissues and structures. It includes information about the heart structure and the function of its electrical conduction system. Since manual analysis of heartbeat rate is time-consuming and prone to errors, automatic recognition of arrhythmias using ECG signals has become an increasingly popular research focus in recent years. Current ECG analysis systems in literature generally have implemented well known machine learning algorithms. Due to the advent of powerful parallel computing hardware and the big data technologies, deep learning has also become a widely preferred technique in healthcare applications. In our study, we use ECG data in MIT-BIH Arrhythmia Database to develop a Convolutional Neural Networks (CNN) which is a deep feed-forward neural network type. The parameter tuned/optimized version of the proposed algorithm on top of the reduced feature dimension is more efficient than state of the art in terms of accuracy. Finally, we also compare the results of the proposed algorithm with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and we provide the corresponding results in related sections. © 2020 IEEE.
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    DSD-R: Deep Learning Based Segmentation for the Detection of R peaks in ECG Signals
    (Ieee, 2022) Celik, Eyupcan; Eren, Furkan; Sahin, Murat; Kilinc, Deniz; Erdemir, Fatih; Den Engelsman, Robert; Gullu, Mehmet Kemal
    Electrocardiography (ECG) is the recording of the electrical activity of the heart through electrodes. ECG signals are crucial in the early diagnosis of numerous cardiac diseases. Therefore, it is very important to read and analyze these signals using state-of-the-art technologies. The regular wave shapes in ECG data are frequently disturbed when certain heart diseases occur and these changes in signals help for detecting the disease. Signal processing and machine learning-based methods are widely used for this purpose. In recent years, deep learning-based methods have become widespread, and they offer promising results. This study aims to segmentation-based detection of R-peak locations in ECG signals. First, the ECG signal is transformed into a Continuous Wavelet Transform (CWT) based scalogram image, and then U-Net-based deep learning architectures are utilized for the segmentation. The comparisons are carried out on MIT-BIH Arrhythmia Database (MIT-DB). Whereas all methods provide promising results, U-Net 3+ model achieves 0.99 in Precision, 0.98 in Recall, 0.99 in F1 score, and 0.98 in Accuracy with the lowest parameter size.