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Öğe Cuff-Less Continuous Blood Pressure Estimation from Electrocardiogram(ECG) and Photoplethysmography (PPG) Signals with Artificial Neural Network(Ieee, 2018) Şentürk, Ümit; Yücedağ, İbrahim; Polat, KemalContinuous blood measurement important information about the health status of the individuals. Conventional methods use a cuff for blood pressure measurement and cannot be measured continuously. In this study, we proposed a system that estimates systolic blood pressure (SP) and diastolic blood pressure (DP) for each heart beat by extracting attributes from ECG and PPG signals. Simultaneous ECG and PPG signals from the PhysioNet Database are pre-processed (denoising, artifact cleaning and baseline wandering) to remove noise and artifacts and segmented into R-R peaks. For each heartbeat, 22-time domain features were extracted from ECG and PPG signals. SP and DP values were estimated by introducing these 22 attributes to the model of Lavenberg-Marquardt artificial neural networks (ANN). Arterial blood pressure (ABP) was also taken from the PhysioNet MIMIC II database along with ECG and PPG signals. ABP signals have been used as targets in the artificial neural network. The system performance has been evaluated by calculating the difference between the estimated ABP values and the actual by the ANN model. The performance value between the predicted SP and actual SP values is -0.14 +/- 2.55 (mean +/- standard deviation) and the performance value between estimated DP and actual DP values is -0.004 +/- 1.6. The obtained results have shown that the proposed model has predicted blood pressure with high accuracy. In this study, SP and DP values can also be measured directly without any calibration in blood pressure estimation.Öğe Histogram-based automatic segmentation of images(Springer, 2016) Küçükkülahlı, Enver; Erdoğmuş, Pakize; Polat, KemalThe segmentation process is defined by separating the objects as clustering in the images. The most used method in the segmentation is k-means clustering algorithm. k-means clustering algorithm needs the number of clusters, the initial central points of clusters as well as the image information. However, there is no preliminary information about the number of clusters in real-life problems. The parameters defined by the user in the segmentation algorithms affect the results of segmentation process. In this study, a general approach performing segmentation without requiring any parameters has been developed. The optimum cluster number has been obtained searching the histogram both vertically and horizontally and recording the local and global maximum values. The quite nearly values have been omitted, since the near local peaks are nearly the same objects. Segmentation processes have been performed with k-means clustering giving the possible centroids of the clusters and the optimum cluster number obtained from the histogram. Finally, thanks to histogram method, the number of clusters of k-means clustering has been automatically found for each image dataset. And also, the histogram-based finding of the number of clusters in datasets could be used prior to clustering algorithm for other signal or image-based datasets. These results have shown that the proposed hybrid method based on histogram and k-means clustering method has obtained very promising results in the image segmentation problems.Öğe A Hybrid Approach to Image Segmentation: Combination of BBO (Biogeography based optimization) and Histogram Based Cluster Estimation(Ieee, 2017) Küçükkülahlı, Enver; Erdoğmuş, Pakize; Polat, KemalImage segmentation is the process of separating objects within an image. Image segmentation can be considered as an important computer vision problem in image sensing where the homogeneous regions in an image can be distinguished with high accuracy. In this study, a two stage hybrid method has been proposed for image segmentation. In the first stage, the Histogram Based Cluster Estimation (HBCE) is used to determine the number of clusters on the image. In the second stage, the cluster numbers determined by the HBCE algorithm are given to the BBO (Biogeography based optimization) algorithm and then image segmentation is performed. In this study, the proposed hybrid image segmentation method was applied to 6 different images taken from Berkeley database and compared with human segmentation results obtained from the same database. To test the performance of the proposed image segmentation method, RI (Rand Index), GCE (Global Consistency Error) and run time as comparison criterion have been used. The proposed method has been compared with other hybrid methods namely HBCE-PSO (Particle Swarm Optimization) and HBCE-k means clustering. When running on 6 different images, the best Rand Index values from the results obtained for all three methods are as follows; HBCE-BBO incorporation: 0.9859, HBCE-PSO incorporation: 0.9856, HBCE-k means incorporation: 0.7570. The results have shown that the HBCE-BBO hybrid method yields better results than the other two hybrid methods used in working with 6 different image segmentations.Öğe A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation(Elsevier, 2018) Ashour, Amira S.; Guo, Yanhui; Küçükkülahlı, Enver; Erdoğmuş, Pakize; Polat, KemalIn this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm. (C) 2018 Elsevier B.V. All rights reserved.Öğe A non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networks(Elsevier Sci Ltd, 2020) Senturk, Umit; Polat, Kemal; Yucedag, IbrahimCardiovascular diseases (CVD) have become the most important health problem of our time. High blood pressure, which is cardiovascular disease, is a risk factor for death, stroke, and heart attack. Blood pressure measurement is commonly used to limit blood flow in the arm or wrist, with the cuff. Since blood pressure cannot be measured continuously in this method, the dynamics underlying blood pressure cannot be determined and are inefficient in capturing symptoms. This paper aims to perform blood pressure estimation using Photoplethysmography (PPG) and Electrocardiography (ECG) signals that do not obstruct the vascular access. These signals were filtered and segmented synchronously from the R interval of the ECG signal, and chaotic, time, and frequency domain features were subtracted, and estimation methods were applied. Different methods of machine learning in blood pressure estimation are compared. Dynamic learning methods such as Recurrent Neural Network (RNN), Nonlinear Autoregressive Network with Exogenous Inputs Neural Networks NARX-NN and Long-Short Term Memory Neural Network (LSTM-NN) used. Estimation results have been evaluated with performance criteria. Systolic Blood Pressure (SBP) error mean +/- standard deviation = 0.0224 +/- (2.211), Diastolic Blood Pressure (DBP) error mean +/- standard deviation = 0.0417 +/- (1.2193) values have been detected in NARX artificial neural network. The blood pressure estimation results are evaluated by the British Hypertension Society (BHS) and American National Standard for Medical Instrumentation ANSI/AAMI SP10: 2002. Finding the most accurate and easy method in blood pressure measurement will contribute to minimizing the errors. (C) 2020 Elsevier Ltd. All rights reserved.Öğe A Novel Blood Pressure Estimation Method with the Combination of Long Short Term Memory Neural Network and Principal Component Analysis Based on PPG Signals(Springer International Publishing Ag, 2020) Senturk, Umit; Polat, Kemal; Yucedag, IbrahimThe worldwide high blood pressure-related mortality rate is increasing. Alternative measurement methods are required for blood pressure measurement. There are similarities between blood pressure and photoplethysmography (PPG) signals. In this study, a novel blood pressure estimation methods based on the feature extracted from the PPG signals have been proposed. First of all, 12-time domain features have extracted from the raw PPG signal. Secondly, raw PPG signals have been applied to Principal Component Analysis (PCA) to obtain 10 new features. The resulting features have been combined to form a hybrid feature set consisting of 22 features. After features extraction, blood pressure values have automatically been predicted by using the Long Short Term Memory Neural Network (LSTM-NN) model. The prediction performance measures including MAE, MAPE, RMSE, and IA values have been used. While the combination of 12-time domain features from PPG signals and LSTM has obtained the MAPE values of 0,0547 in the prediction of blood pressures, the combination of 10-PCA coefficients and LSTM has achieved the MAPE value of 0,0559. The combination model of all features (22) and LSTM has obtained the MAPE values of 0,0488 in the prediction of blood pressures. The achieved results have shown that the proposed hybrid model based on combining PCA and LSTM is very promising in the prediction of blood pressure from PPG signals. In the future, the proposed hybrid method can be used as a wearable device in the measurement of blood pressure without any calibration.Öğe A Novel ML Approach to Prediction of Breast Cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier(Ieee, 2018) Polat, Kemal; Şentürk, ÜmitBreast cancer is the second most common cancer in our country and in the world. In this study, a breast cancer data set was formed from the findings obtained from experiments conducted in the city of Coimbra of Portugal. There are two sets of data (52 data: healthy group, 64 data belong to patient group) and 9 features in the breast cancer data set of 116 data, both healthy and patient. These nine features are: Age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, MCP-1. In the proposed method, a three-step hybrid structure is proposed to detect the presence of breast cancer. In the first step, the dataset was first normalized by the MAD normalization method. In the second step, k-means clustering (KMC) based feature weighting has been used for weighting the normalized data. Finally, the AdaBoostM1 classifier has been used to classify the weighted data set. Only the combination the AdaBoostM1 classifier with MAD normalization method yielded a 75% classification accuracy in the detection of breast cancer, whereas the hybrid approach achieved 91.37% success. These results show that the proposed system could be used safely to detect breast cancer.Öğe PC-Based Detection of ECG Signals, Decomposition and Analysis(Ieee, 2018) Şentürk, Ümit; Yücedağ, İbrahim; Polat, Kemal; Varol, H. SelçukThe 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.Öğe Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals(Ieee, 2018) Şentürk, Ümit; Yücedağ, İbrahim; Polat, KemalIn this study, a new hybrid prediction model was proposed by combining ECG (Electrocardiography) and PPG (Photoplethysmographic) signals with a repetitive neural network (RNN) structure to estimate blood pressure continuously. The proposed method consists of two steps. In the first step, a total of 22 time-domain attributes were obtained from PPG and ECG signals to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. In the second phase, these time-domain attributes are set as input to the RNN model and then the blood pressure is estimated. Within the RNN structure, there are two-way long short-term memory BLSTM (Bidirectational Long-Short Term Memory), LSTM and ReLU (Rectified-Linear unit) layers. The bidirectional LSTM layer has been used to remove the negative affects the blood pressure value of past and future effects of nonlinear physiological changes. The LSTM layers has ensured that learning is deep and that mistakes made are reduced. The ReLU layer has been allowed the neural network to quickly reach its conclusion. The same ECG and PPG signals obtained from the database have been cleared from noise and artifacts. And then ECG and PPG signals have been segmented according to peak values of these signals. The results have shown that RMSE (Root Mean Square Error) between the estimated SBP and the measured SBP with RNN model was 3.63 and the RMSE between the estimated DBP and the measured DBP values was 1.48 with RNN model. It has been seen that the used model has a more learning ability. Thanks to the proposed method, a calibration free blood pressure measurement system using PPG and ECG signals, was developed.Öğe Towards wearable blood pressure measurement systems from biosignals: a review(2019) Şentürk, Ümit; Polat, Kemal; Yücedağ, İbrahimBlood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, isthe cause of nearly 13% of mortality all over the world. Blood pressure is not only measured in the medical environment,but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systemswith low error rates have been developed besides the new technologies and algorithms. Blood pressure measurementsare differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurementmethods. Although IBP measurement provides the most accurate results, it cannot be used in daily life because itcan only be performed by qualified medical staff with specialized medical equipment. NIBP measurement is based onmeasuring physiological signals taken from the body and producing results with decision mechanisms. Oscillometric,pulse transit time (PTT), pulse wave velocity, and feature extraction methods are mentioned in the literature as NIBP.In the oscillometric method of the sphygmomanometer, an electrocardiogram is used in PTT methods as a result of thecomparison of signals such as electrocardiography, photoplethysmography, ballistocardiography, and seismocardiography.The increase in the human population and worldwide deaths due to the highly elevated blood pressure makes the needfor precise measurements and technological devices more clear. Today, wearable technologies and sensors have beenfrequently used in the health sector. In this review article, the invasive and noninvasive blood pressure methods,including various biosignals, have been investigated and then compared with each other concerning the measurement ofcomfort and robust estimation.Öğe TSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysis(Ieee, 2018) Kayaalp, Fatih; Başarslan, Muhammet Sinan; Polat, KemalAttribute 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 Rcorrelation coefficient-based attribute selection (RCBAS) and the rho-correlation coefficient-based attribute selection (rho 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, rho-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.
