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Öğe Cardiotocography Analysis based on Segmentation-based Fractal Texture Decomposition and Extreme Learning Machine(Ieee, 2017) Comert, Zafer; Kocamaz, Adnan FatihFetal heart rate (FHR) has notable patterns for the assessment of fetal physiology and typical stress conditions. FHR signals are obtained using cardiotocography (CTG) devices also providing uterine activities simultaneously and fetal movements. In this study, a total of 88 records consisting of 44 normal and 44 hypoxic fetuses instances obtained from publicly available CTU-UHB database have been considered. The basic morphological features supporting clinical diagnosis, the powers of 4 different spectral bands and Lempel Ziv complexity have been used to define FHR signals. Also, it has been proposed to use segmentation-based fractal texture analysis (SFTA) to identify the signals more accurately. The obtained feature set was applied as the input to extreme learning machine (ELM) with 5-fold cross-validation method. According to experimental results, 79.65% of accuracy, 79.92% of specificity, and 80.95% of sensitivity were obtained. It was observed that the SFTA offers useful statistical features to distinguish normal and hypoxic fetuses.Öğe Classification and Comparison of Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machine(Ieee, 2016) Comert, Zafer; Kocamaz, Adnan Fatih; Gungor, SamiCardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning Repository. The prominent features, such as baseline, the number of acceleration and deceleration patterns, and variability recommended by International Federation of Gynecology and Obstetrics (FIGO) have also taken into account during CTG analysis. The features were applied as the input to feedforward neural network (ANN) and Extreme Learning Machine (ELM) to classify FHR patterns in this study. FHR is recently divided into three classes as normal, suspicious and pathological. According to the results of this study, the accuracy of classification of ANN and ELM were obtained as 91.84% and 93.42%, respectively.Öğe Determination of QT Interval on Synthetic Electrocardiogram(Ieee, 2015) Comert, Zafer; Kocamaz, A. FatihElectrocardiography is widely used for diagnosis and assessment of cardiovascular diseases. Heart rate is a major determinant of ventricular action potential. Therefore, the QT interval varies to be inversely proportional to heart rate. The need of the calculation and correction of the QT interval arises from this situation. In this study, synthetic electrocardiography signals have been passed through a preprocessing process and reduction of noise has been provided by using principal component analysis. Then, signal features have been extracted and finally QT intervals have been analyzed with Bazett method.Öğe Efficient approach for digitization of the cardiotocography signals(Elsevier, 2020) Comert, Zafer; Sengur, Abdulkadir; Akbulut, Yaman; Budak, Umit; Kocamaz, Adnan Fatih; Bajaj, VarunCardiotocography (CTG) is generally provided on printed traces, and digitization of CTG signal is important for forthcoming assessments. In this paper, a new algorithm relies on the box-counting method is offered for the digitization of the CTG signals from CTG printed traces. The introduced algorithm inputs the CTG printed traces and outputs the digital fetal heart rate (FHR) and uterine contraction (UC) signals. The proposed method initially extracts the CTG signal image and gridded background image. Retrieving of the FHR and UC signals on the gridded background disrupts the background grids. So, we employ an algorithm to fix the degraded lines in the gridded background. After the line fixing operation, the boxes in the horizontal and vertical axes are counted for determining the calibration parameters. A set of specific equations are used to determine the calibration parameters. The signal extraction is performed on by red channel thresholding of input CTG printing images. An open-access CTG intrapartum database comprises 552 samples is used in the experiment. As a result, the average correlation coefficients of FHR and UC signals are 0.9811 +/- 0.0251 and 0.9905 +/- 0.0126, respectively. (C) 2019 Elsevier B.V. All rights reserved.Öğe Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach(Springer International Publishing Ag, 2019) Comert, Zafer; Kocamaz, Adnan FatihElectronic fetal monitoring (EFM) device which is used to record Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals simultaneously is one of the significant tools in terms of the present obstetric clinical applications. In clinical practice, EFM traces are routinely evaluated with visual inspection by observers. For this reason, such a subjective interpretation has been caused various conflicts among observers to arise. Although the existing of international guidelines for ensuring more consistent assessment, the automated FHR analysis has been adopted as the most promising solution. In this study, an innovative approach based on deep convolutional neural network (DCNN) is proposed to classify FHR signals as normal and abnormal. The proposed method composes of three stages. FHR signals are passed through a set of preprocessing procedures in order to ensure more meaningful input images, firstly. Then, a visual representation of time-frequency information, spectrograms are obtained with the help of the Short Time Fourier Transform (STFT). Finally, DCNN method is utilized to classify FHR signals. To this end, the colored spectrograms images are used to train the network. In order to evaluate the proposed model, we conducted extensive experiments on the open CTU-UHB database considering the area under the receiver operating characteristic curve and other several performance metrics derived from the confusion matrix. Consequently, we achieved encouraging results.Öğe Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach(Elsevier Sci Ltd, 2019) Altuntas, Yahya; Comert, Zafer; Kocamaz, Adnan FatihMaize is one of the most significant grains cultivated all over the world. Doubled-haploid is an important technique in terms of advanced maize breeding, modern crop improvement and genetic programs, since this technique shortens the breeding period and increases breeding efficiency. However, the selection of the haploid seeds is a major problem of this breeding technique. This process is frequently conducted manually, and this unreliable situation leads to loss of time and labor. Inspired by the recent successes of deep transfer learning, in this study, we approached this problem as a computer vision task to provide a nondestructive, rapid and low-cost model. To achieve this objective, we adopted convolutional neural networks (CNNs) to recognize haploid and diploid maize seeds automatically through a transfer learning approach. More specifically, AlexNet, VVGNet, GoogLeNet, and ResNet were applied for this specific task. The experimental study was carried out using a new dataset consisting of 1230 haploid and 1770 diploid maize seed images. The samples in the dataset were classified considering a marker-assisted selection, known as the R1-nj anthocyanin marker. To measure the success of the CNN models, we utilized several performance metrics, such as accuracy, sensitivity, specificity, quality index, and F-score derived from the confusion matrix and receiver operating characteristic curves. According to the experimental results, the CNN models ensured promising results, and we achieved the most efficient results via VGG-19. The accuracy, sensitivity, specificity, quality index, and F-score of VGG-19 were 94.22%, 94.58%, 93.97%, 94.27%, and 93.07%, respectively. Consequently, the experimental results proved that CNN models can be a useful tool in recognizing haploid maize seeds. Furthermore, we conclude that this approach is significantly superior to machine learning-based methods and conventional manual selection.Öğe Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques(Ieee, 2018) Altuntas, Yahya; Kocamaz, Adnan Fatih; Comert, Zafer; Cengiz, Rahime; Esmeray, MesutDoubled haploid (DH) technique is used effectively in maize breeding. This technique is superior to conventional maize breeding in terms of both time and homozygosity. One of the important processes in DH technique is the selection of haploid seeds. The most common method for selecting haploids is the RI-nj (Navajo) color marker. This color marker appears in the seed endosperm and embryo. Only endosperm color seeds are selected and continued to the germination stage This selection is usually done manually. The automation of haploid seed selection will increase success and reduce the labor and time In this study, we used 87 haploid and 326 diploid maize seeds as dataset. Texture features of maize seeds embryos were used These features were obtained from gray level co-occurrence matrix. The feature vectors are classified using decision trees, k-nearest neighbors and artificial neural networks. The classification performance of machine learning tecniques was tested by using 10 fold cross-validation method As a result of the test, the best performance was measured in decision tree with the classification success rate as 84.48%.Öğe The Influences of Different Window Functions and Lengths on Image-based Time-Frequency Features of Fetal Heart Rate Signals(Ieee, 2018) Comert, Zafer; Boopathi, A. Manivanna; Velappan, Subha; Yang, Zhang; Kocamaz, Adnan FatihIn the clinical practice, the fetal distress conditions such as hypoxia are detected routinely during antepartum and even intrapartum periods with the help of electronic fetal monitoring device, often called Cardiotocography (CTG). Due to the noticeable advances in signal processing, pattern recognition, machine learning techniques and the introduction of the quantitative diagnostic indices, the automated CTG analysis has become a quite essential tool. In this study, we come up with a new investigation on the influences of different window functions on image-based time-frequency (IBTF) features of fetal heart rate (FHR) signals for fetal hypoxia detection. In addition to the traditionally used morphological features, the spectrogram images provided by Short Time Fourier Transform (STFT) were taken into account with different windows functions such as Hamming, Hann, Kaiser, and Blackman as well as different windows lengths. Then, the spectrogram images were converted into 8-bits gray-scale images and IBTF features were obtained using Gray Level Co-occurrence Matrix (GLCM). At the end of the feature extraction stage for signal representation, we achieved a quite large feature set, and we employed genetic algorithm (GA) and support vector machine (SVM) classifier in order to reveal the most relevant features. The whole experiments were performed on an open CTU-UHB intrapartum CTG database. The experimental results show that the IBTF features have relatively increased the classification performance. All window functions ensured encouraging results. Furthermore, the GA ensured the determination of the 7 most relevant features. Thus, the dimension of feature space was reduced from 28 to 7. Moreover, the classification success increased. Consequently, the most efficient performances (Quality Index = 73.45%) were achieved with Hamming and Kaiser window functions.Öğe A Novel Software for Comprehensive Analysis of Cardiotocography Signals CTG-OAS(Ieee, 2017) Comert, Zafer; Kocamaz, Adnan FatihThe research interest in fetal heart rate (FHR) monitoring dates back to the 1960s, and the breakthrough on fetal surveillance has been seen during the 1990s with computerized systems. Notwithstanding the general use of cardiotocography (CTG) in fetal monitoring, the assessment of fetal well-being exhibits a significant inter-and even intra-observer variability. Computerized CTG analysis has seen as the most promising way to tackle of the main shortcomings of visual CTG assessment. In this study, a novel software developed for research purposes is introduced. The software named as CTG Open Access Software (CTG-OAS) characterizes FHR signals by using comprehensive features obtained from different fields such as morphological, linear, nonlinear, time-frequency, discrete wavelet transform, and image-based time-frequency domains. The software also covers the main procedures which are necessary for the context of machine learning. More specifically, CTG-OAS presents several tools for performing the preprocessing, feature extraction, feature selection, and classification processes. The proposed software was practiced on CTU-UHB database with 552 raw CTG samples. In addition, a case study with Support Vector Machine classifier was performed in the study via CTG-OAS. According to experimental results, statistical parameters were obtained as accuracy equal to 87.97%, sensitivity equal to 89.04%, specificity equal to 81.36% and, quality index equal to 85.11%.Öğe Open-access software for analysis of fetal heart rate signals(Elsevier Sci Ltd, 2018) Comert, Zafer; Kocamaz, Adnan FatihCardiotocography (CTG) comprises fetal heart rate (FHR) and uterine contraction (UC) signals that are simultaneously recorded. In clinical practice, a visual examination is subjectively performed by observers depending on the guidelines to evaluate CTG traces. Owing to this visual assessment, the variability in the interpretation of CTG between inter-and even intra-observers is considerably high. In addition, traditional clinical practice involves different human factors that distort the quantitative quality of the evaluation. Automated CTG analysis is the most promising way to tackle the main shortcomings of CTG by providing reproducibility of the evaluation as well as the quantitative results. In this study, open access software (CTG-OAS) developed with MATLAB is introduced for the analysis of FHR signals. The software contains important processes of the automated CTG analysis, from accessing the database to conducting model evaluations. In addition to traditionally used morphological, linear, nonlinear, and time-frequency features, the developed software introduces an innovative approach called image-based time-frequency features to characterize FHR signals. All functions of the software are well documented, and it is distributed freely for research purposes. In addition, an experimental study on the publicly accessible CTU-UHB database was performed using CTG-OAS to test the reliability of the software. The experimental study obtained results that included an accuracy of 77.81%, sensitivity of 76.83%, specificity of 78.27%, and geometric mean of 77.29%. These fairly promising results indicate that the software can be a valuable tool for the analysis of CTG signals. In addition, the results obtained using CTG-OAS can be easily compared to different algorithms. Moreover, different experimental setups can be designed using the tools provided by the software. Thus, the software can contribute to the development of new algorithms. (C) 2018 Elsevier Ltd. All rights reserved.Öğe Performance Evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for Computerized Hypoxia Detection and Prediction(Ieee, 2018) Comert, Zafer; Yang, Zhang; Velappan, Subha; Boopathi, A. Manivanna; Kocamaz, Adnan FatihThis study proposes a new model relying on Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) in order to detect fetal hypoxia by using Cardiotocography (CTG) signals. We processed one well known open access intrapartum CTU-UHB database to find if our model could outperform the state-of-the art models. The model consists of three key stages: (1) Preprocessing, (2) Features extraction using EMD and DWT, (3) Classification with Support Vector Machine (SVM). Also, we present a comparative experimental study to measure the performance of SVM classifier depending on feature extraction methods. As a result, EMD and DWT have been found as useful methods for fetal hypoxia detection. Also, SVM classifier utilizing a combination of DWT and morphological features achieved the highest performance. Furthermore, DWT features produced more successful results than EMD features in terms of the classification success. Consequently, the proposed model ensured sensitivity of 57.42% and specificity of 70.11%.Öğe Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models(Springer, 2019) Comert, Zafer; Sengur, Abdulkadir; Budak, Umit; Kocamaz, Adnan FatihIntroduction Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. Materials and Methods Feature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtained from an open-access CTU-UHB intrapartum CTG database. The signals are divided into two classes as normal and hypoxic considering umbilical artery pH value (pH < 7.20) measured after delivery. A comprehensive diagnostic feature set forming the features obtained from morphological, linear, nonlinear, time-frequency and image-based time-frequency domains is generated first. Then, combinations of the feature selection algorithms and machine learning models are evaluated to achieve the most effective features as well as high classification performance. Results The experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively. Conclusion Consequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.Öğe Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment(Pergamon-Elsevier Science Ltd, 2018) Comert, Zafer; Kocamaz, Adnan Fatih; Subha, VelappanCardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. in this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.Öğe Using Wavelet Transform for Cardiotocography Signals Classification(Ieee, 2017) Comert, Zafer; Kocamaz, Adnan FatihAs a fetal surveillance technique, cardiotocography (CTG) involves fetal heart rate (FHR), uterine contraction activities, and fetal movements. CTG is practiced as a primary diagnostic test throughout the world to identify events that may pose a risk to the fetus during pregnancy and delivery. In this work, FHR signals carrying vital information on fetus were analyzed by using Haar (haar), Daubechies (db5), and Symlets (sym5) mother wavelet families between levels 1 and 12. The traditionally used morphological and linear features are obtained from FHR. Also, p-norm, Frobenius form, infinity, and negative infinity norms which are obtained separately from the each of the wavelet components were used as a feature to support the classification. The obtained features were applied as an input to k nearest neighbors (kNN) and artificial neural network (ANN) classifiers in order to discriminate the normal and hypoxic fetuses. According to experimental results, 90.51% and 90.21% classification success on the discrimination of normal and hypoxic fetuses were achieved by using haar at level 4 and kNN.
