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Öğe Alternatively new signal for sleep staging processing in patients with obstructive sleep apnea: photoplethysmography signal(Ieee, 2016) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Polat, Kemal; Bilgin, CahitDiagnosis of Obstructive Sleep Apnea is done by expert doctors by examining biological signals which is obtain from the patient help of polysomnography device. Review, consists of two stages which are sleep staging and respiratory scoring. Sleep staging is done using Electroencephalogram, Electromyogram and Electrooculogram signals. Derivation of signal format gives discomfort to the patient. In order to connect the electrodes to the patient, there is a need expert technicians. In addition, the system is not suitable for use at home. When considering all these disadvantages, practical system is needed to make sleep staging. In this study, Photoplethysmography signal use will be suggested for alternative to the signals used in sleep staging process. Photoplethysmography signal can measure through the skin of any part of the body with noninvasive method. In the study, the characteristic features of Photoplethysmography signals were analyzed whether it is distinctive for sleep and wake statistically by means of Mann-Whitney U Test. According to the results obtained p < 0.05 and all properties are meaningful for sleep-wake. All features can be used as distinctive for the sleep-wakefulness are considered and also a practical sleep staging system be realized using Photoplethysmography signal.Öğe Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques(Springer, 2017) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, KemalObstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann-Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.Öğe Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques(Springer, 2018) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, KemalIt is extremely significant to identify sleep stages accurately in the diagnosis of obstructive sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a practical and applicable method. For this purpose , the signal of heart rate variability (HRV) has been derived from photoplethysmography (PPG). Feature extraction has been made from PPG and HRV signals. Afterward, the features, which will represent sleep and wakefulness in the best possible way, have been selected using F-score feature selection method. The selected features were classified with k-nearest neighbors classification algorithm and support vector machines. According to the results of the classification, the classification accuracy rate was found to be 73.36 %, sensivity 0.81, and specificity 0.77. Examining the performance of the classification, classifier kappa value was obtained as 0.59, area under an receiver operating characteristic value as 0.79, tenfold cross-validation as 77.35 %, and F-measurement value as 0.79. According to the results accomplished, it was concluded that PPG and HRV signals could be used for sleep staging process. It is a great advantage that PPG signal can be measured more practically compared to the other sleep staging signals used in the literature. Improving the systems, in which these signals will be used, will make diagnosis methods more practical.Öğe Feature extraetion for biometrie reeognition with photoplethysmography signals(2013) Kavsaoğlu, Ahmet Reşit; Polat, Kemal; Bozkurt, Mehmet Recep; Muthusamy, HariharanPhotoplethysmography (PPG) signals stand out due to features such as readily accessible, high reliability and confidentiality, the ease of use etc. among bio-signals. The feasibility studies carried out on the PPG signals demonstrated that PPG signals contained important features for human recognition and were the availability of biometric identification systems. In this study, twenty new features were extracted from PPG signal as a preliminary study intended to biometric recognition. PPG signals with 10 seconds were recorded from five healthy people using SDPPG (second derivative PPG) data acquisition card. To remove the noise from received raw PPG signals, a FIR low pass filtering with 200 points and 10 Hz cut-off frequency was designed. These twenty new features were obtained from filtered PPG signal and its second derivative. PPG signal with 10 seconds contains eight periods and twenty characteristic features in each person must not change within an individual over a period. This feature symbolizes the consistency in the identification of a person. To test the performance of biometrie recognition system, the k-NN (k-nearest neighbor) classifier was used and achieved 95% of recognition success rate using lO-fold cross validation with twenty new features. The obtained results showed that the developed biometric recognition system based on PPG signal were very promising. © 2013 IEEE.Öğe An innovative peak detection algorithm for photoplethysmography signals: An adaptive segmentation method(2016) Kavsaoğlu, Ahmet Reşit; Polat, Kemal; Bozkurt, Mehmet RecepThe purpose of this paper is twofold. The first purpose is to detect M-peaks from raw photoplethysmography (PPG) signals with no preprocessing method applied to the signals. The second purpose is to estimate heart rate variability (HRV) by finding the peaks in the PPG signal. HRV is a measure of the fluctuation of the time interval between heartbeats and is calculated based on time series between strokes derived from electrocardiogram (ECG), arterial pressure (AP), or PPG signals, separately. PPG is a method widely used to measure blood volume of tissue on the basis of blood volume change in every heartbeat. In the estimation of the HRV signal from the PPG signal, HRV is calculated by measuring the time intervals between the peak values in the PPG signal. In the present paper, a novel peak detection algorithm was developed for PPG signals. Finding peak values correctly from PPG signals, the HRV signal can be estimated. This peak detection algorithm has been called an adaptive segmentation method (ASM). In this method, the PPG signals are first separated into segments with sample sizes and then the peak points in these signals are detected by comparing with maximum points in these segments. To evaluate the estimated pulse rate and HRV signals from PPG, Poincare plots and time domain features including minimum, maximum, mean, mode, standard deviation, variance, skewness, and kurtosis values were used. Our experimental results demonstrated that ASM could be even used both in the estimation of HRV signals and to detect the peaks from raw and noisy PPG signals without a pre-processing method.Öğe Investigation of Effects of Time Domain Features of the Photoplethysmography (PPG) Signal on Sleep Respiratory Arrests(Ieee, 2015) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Polat, Kemal; Bilgin, CahitObstructive Sleep Apnea Syndrome is one of the major diseases of our century. Diagnosis of this disease is very difficult. The reason of the difficulty is the complexity of the system. AHI index is calculated for the diagnosis of the disease as a result of respiratory scoring process. However it is necessary to use four different signals to do this operation. The reduction of signal measurements that discomforts the patient or using different signal measurements will provide the patient to sleep closer to his/her natural sleep environment which will increase the accuracy rate of the studies that are made in literature. Changes occurring in photoplethysmography signal during respiratory events were examined. In this study, the patient data that was used respiratory events were scored Changes on the photoplethysmography signals were examined. The data that were used in this study were respiratory events tagged apnea and hypopnea. For control, the photoplethysmography signals were recorded during normal breathing in sleep. The data were analyzed using the one-way analysis of variance method. According to the obtained results, the photoplethysmography signals have significant changes in between the apnea-hypopnea classes and normal classes during respiratory pauses in sleep. However, there are not significant differences between apnea and hypopnea classes. The study concluded that, in the scoring of respiratory events, photoplethysmography could be used more efficiently using a computer software.Öğe A novel feature ranking algorithm for biometric recognition with PPG signals(Pergamon-Elsevier Science Ltd, 2014) Kavsaoglu, Ahmet Reşit; Polat, Kemal; Bozkurt, Mehmet RecepThis study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.Öğe Parkinson hastalığı teşhisi için makine öğrenmesi tabanlı yeni bir yöntem(2020) Esmer, Sadullah; Uçar, Muhammed Kürşad; Çil, İbrahim; Bozkurt, Mehmet RecepParkinson hastalığı (PH), dopamin üreten beyin hücrelerinin ölmesiyle ya da zarar görmesiyle ortaya çıkan bir beyin hastalığıdır. Böyle bir durumda, beyin normal fonksiyonlarını yerine getiremez. PH, konuşma, yürüme ve yazma gibi insan hareketlerini olumsuz olarak etkiler. Bu hastalığın teşhisinde detaylı tıbbi öykü, geçmiş tedavi öyküsü, fiziksel testler ve bazı kan testleri ile beyin filmleri istenilmektedir. Bu işlemler maliyetli ve meşakkatli olabildiği için daha az maliyetli ve daha kolay yapılabilen teşhis bu noktada önem kazanmaktadır. Bu çalışmada doktorun kararına destek olabilmesi için 252 bireyden alınan ses verileri ile PH’ın teşhis edilebilmesi amaçlanmıştır. Verilerden daha iyi sonuç alabilmek için bazı ön işlemler uygulanmıştır. Verilerde dengeleme işlemi yapılmış ve sistematik örnekleme metodu ile işleme alınacak veriler belirlenmiştir. Öznitelik seçme algoritması ile özniteliklerin etiket üzerindeki etki gücü hesaplanıp bazı veri grupları oluşturulmuştur. Sınıflandırma algoritmalarından Karar ağacı, Destek Vektör Makineleri ve K En Yakın Komşu Algoritması kullanılıp, performans değerlendirme kriterleri - bunlar; Doğruluk Oranı, Duyarlılık, Özgünlük, F-Ölçümü, Kappa, Auc - değerlendirilmiştir. En yüksek performans değerine sahip veri grubu ve kullanılan sınıflandırma algoritması belirlenip model oluşturulmuştur. Model en ilgiliden en ilgisize doğru sıralanmış veri setinin %45’inden ve Destek vektör makineleri algoritması kullanılarak oluşturulmuştur. Performans kriterlerinde %85 doğruluk oranı ve diğer kriterlerde de olumlu sonuçlar elde edilmiştir. Böylece PH olma ihtimali olan bireyin ses kayıtlarından oluşturulan veri seti ve uygulanan model yardımı ile doktora tıbbi karar destek sağlanacağı anlaşılmıştır.Öğe Real time heart rate detection using non-contact photoplethysmography signals(IEEE Computer Society, 2014) Kavsaoğlu, Ahmet Reşit; Polat, Kemal; Bozkurt, Mehmet RecepHeart is contracted rhythmically so as to drive nutrients and oxygen necessary for life through our organs with blood arteries. The frequency for the rhythmic contraction of heart just as a pump is called heart rate (HR). Heart rate variation (HRV) is a measure of a fluctuation of time interval between heart beats. HRV is calculated considering electrodiagram (ECG) signals, arterial blood pressure signals or photoplethysmography (PPG) signals-derived time series of in-between heart beats. HRV is used as a significant indicator for the detection of healthiness and sickness state. Such pathological cases as high blood pressure, heart failure, and septic shock can be diagnosed using HRV. Therefore, accurate and rapid detection of HR is essential to correct diagnosis. In this study, real-time heart rate detection was derived from contactless PPG signals. PPG calling for contact with skin becomes useless in case of tissue scars or burns. In such cases, the use of contactless PPG is superior. Contactless PPG consists of a light source and a camera that senses reflection or transmittance of the light source. Camera images obtained were processed through an interface prepared in the MATLABTM GUI setting, and real-time heart rate detection was carried out. © 2014 IEEE.Öğe Retraction note: Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques (Neural Computing and Applications, (2017), 28, 10, (2931-2945), 10.1007/s00521-016-2617-9)(Springer Science and Business Media Deutschland GmbH, 2024) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, KemalThe Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Authors Muhammed Kürşad Uçar, Mehmet Recep Bozkurt and Kemal Polat disagree with this retraction. Author Cahit Bilgin has not responded to correspondence regarding this retraction. © The Natural Computing Applications Forum 2024.Öğe Retraction Note: Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques(Neural Comput Appl, (2018), 29, (1–16), 10.1007/s00521-016-2365-x)(Springer Science and Business Media Deutschland GmbH, 2024) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, KemalThe Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Authors Muhammed Kürşad Uçar, Mehmet Recep Bozkurt and Kemal Polat disagree with this retraction. Author Cahit Bilgin has not responded to correspondence regarding this retraction. © The Natural Computing Applications Forum 2024.
