Yazar "Polat, K." seçeneğine göre listele

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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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    PC-Based Detection of ECG Signals, Decomposition and Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2019) Senturk, Ü.; Yüceda?, I.; Polat, K.; Varol, H. S.
    The number of deaths from heart diseases is increasing rapidly today. Deaths related to heart diseases are caused by heart arrhythmia. Anomalies in the heart can cause a sudden heart attack or permanent damage to the heart. In this study, we designed a new electrocardiography (ECG) device. It was used to diagnose automatic heart anomalies by taking signals from the body surface with the designed device. ECG signals are used for R peak detection using Statistical and Pan-Tompkins analysis methods. R peaks often plays an important role in the diagnosis of heart disease. In the statistical method, which is a new R peak detection method, the digital bandpass filter, the detection of the maximum R wave, the determination of the border point according to the detected R wave, the detection of the r waves by the determined border on the signal will be carried out. In the Pan-Tompkins analysis method, the processes of discarding DC components, bandpass filter, derivation receiver, taking frames, integrating movable window, and determining R waves are performed respectively. 98.9% success in the statistical analysis method performed on the designed ECG instrument measurements and sample signals obtained from the MIT-BIH ECG data bank, and 98.7% accuracy in the Pan-Tompkins analysis method. © 2018 IEEE.
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    TSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2019) Kayaalp, F.; Başarslan, M. S.; Polat, K.
    Attribute selection has a significant effect on the performance of the machine learning studies by selecting the attributes having significant effect on result, reducing the number of attributes, and reducing the calculation cost. In this study, a new attribute selection method which is a combination of the R-correlation coefficient-based attribute selection (RCBAS) and the ?-correlation coefficient-based attribute selection (?CBAS) called the Two-Stage Correlation-Based Attribute Selection (TSCBAS) is proposed to select significant attributes. The proposed attribute selection method has been applied to customer churn prediction on a telecommunications dataset for performance evaluation. The dataset used in the study includes real customer call records details for the years 2013 and 2014 obtained from a major telecommunications company in Turkey. Apart from the proposed attribute selection method, four different methods named Rcorrelation coefficient-based attribute selection, ?-correlation coefficient-based attribute selection, ReliefF, and Gain Ratio have been used for creating five datasets. After that, four classifier algorithms including Random Forest, C4.5 Decision Tree, Naive Bayes and AdaBoost.M1 have been applied. The obtained results have been compared according to the performance metrics comprising Accuracy (ACC), Sensitivity (TPR), Specificity (SPC), F-measure (F), AUC (area under the ROC curve), and run-time. The results of the comparisons show that the proposed attribute selection algorithm outperforms the state of the art methods on customer churn prediction. © 2018 IEEE.