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Öğe A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE(2022) ARI, Berna; Alçin, Ömer Faruk; Sengur, AbdulkadirThe method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure.Öğe Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals(Elsevier B.V. All, 2022) Arı, Berna; Sobahi, Nebras; Alçin, Ömer Faruk; Sengur, Abdulkadir; Acharya, U.RajendraAutism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application.Öğe Approach based on wavelet packet transform and 1D-RMLBP for drowsiness detection using EEG(Wiley-Blackwell, 2020) Alçin, Ömer FarukEarly drowsiness detection may be crucial for the vehicle alertness system. Towards this, wearable technology, camera-based biophysical signals like electroencephalogram (EEG) approaches are utilised. In this Letter, the EEG-based approach is proposed to detect drowsiness. The proposed method consists of random sampling-based artificial signal augmentation, wavelet packet transform decomposition, logarithmic energy entropy, and one-dimensional region mean local binary pattern (1d-RMLBP) based feature extraction and classifier. k-Nearest neighbour and support vector machine classifiers are employed to detect the drowsiness. The MIT/BIH polysomnographic dataset has been used to test the proposed model. The proposed method has superior performance than the other methods using the same data set. The experimental results demonstrate that the proposed model could efficiently detect drowsiness from polysomnographic EEG signals.Öğe Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns(Institute of Physics Publishing, 2020) Alçin, Ömer Faruk; Budak, Ümit; Aslan, Muzaffer; Akbulut, Yaman; Cömert, Zafer; Akpınar, Muhammed H.; Şengür, AbdulkadirPhysical action recognition is a hot topic in human-machine interactions. It has potential uses in helping disabled people and in various robotic applications. Electromyography (EMG) signals measure the electrical activity of the muscular systems involved in physical actions. In this chapter, an efficient approach is developed for physical action recognition in humans based on EMG signals. The proposed method is composed of signal decomposition, feature extraction and feature classification. The signal decomposition is carried out using the wavelet packet transform (WPT). The WPT successively decomposes an input signal into its approximation and detail coefficients, which offers a more productive signal analysis. A one-dimensional local binary pattern (LBP) is used to code the approximation and detail coefficients of the decomposed EMG signals. The histogram of the LBP is used as the feature vectors of the EMG physical action classes. The support vector machine (SVM), decision tree, linear discriminant, k-nearest neighbors (k-NN), boosted and bagged tree ensemble classifiers are used in the classification stage. A dataset taken from the UCI machine learning repository is used in the experiments. The Delsys EMG apparatus, which has eight electrodes, is used to record the surface EMG signals. Each class of the dataset contains three male subjects and one female subject. The dataset contains ten physical actions, namely hugging, jumping, bowing, clapping, handshaking, running, sitting, standing, walking and waving. The experiments are carried out on each electrode with a ten-fold cross-validation test, and the average accuracy score is calculated. The experimental results show that the proposed method is quite efficient in EMG signal classification. The calculated average accuracy is 100% for each electrode.Öğe Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors(Springer, 2022) Gündüz, Emrah; Alçin, Ömer Faruk; Kızılay, Ahmet; Yıldırım, İsmail OkanPurpose: To create a new artificial intelligence approach based on deep learning (DL) from multiparametric MRI in the differential diagnosis of common parotid tumors. Methods: Parotid tumors were classified using the InceptionResNetV2 DL model and majority voting approach with MRI images of 123 patients. The study was conducted in three stages. At stage I, the classification of the control, pleomorphic adenoma, Warthin tumor and malignant tumor (MT) groups was examined, and two approaches in which MRI sequences were given in combined and non-combined forms were established. At stage II, the classification of the benign tumor, MT and control groups was made. At stage III, patients with a tumor in the parotid gland and those with a healthy parotid gland were classified. Results: A stage I, the accuracy value for classification in the non-combined and combined approaches was 86.43% and 92.86%, respectively. This value at stage II and stage III was found respectively as 92.14% and 99.29%. Conclusions: The approach presented in this study classifies parotid tumors automatically and with high accuracy using DL models.Öğe Deep rhythm and long short term memory-based drowsiness detection(Elsevier, 2021) Türkoğlu, Muammer; Alçin, Ömer Faruk; Aslan, Muzaffer; Al-Zebari, Adel; Şengur, AbdülkadirIn this paper, a deep-rhythm-based approach is proposed for the efficient detection of drowsiness based on EEG recordings. In the proposed approach, EEG images are used instead of signals where the time and frequency information of the EEG signals are incorporated. The EEG signals are converted to EEG images using the time-frequency transformation method. The Short-Time-Fourier-Transform (STFT) is used for this transformation due to its simplicity. The rhythm images are then extracted by dividing the EEG images based on frequency intervals. EEG signals contain five rhythms, namely Delta rhythm (0–4?Hz), Theta rhythm (4–8?Hz), Alpha rhythm (8–12?Hz), Beta rhythm (12–30?Hz), and Gamma rhythm (30–50?Hz). From each rhythm image, deep features are extracted based on a pre-trained convolutional neural network (CNN) model, with pre-trained residual network (ResNet) models such as ResNet18, ResNet50, and ResNet101. The obtained deep features from each rhythm image are fed into the Long-Short-Term-Memory (LSTM) layer, and the LSTM layers are then sequentially connected to each other. After the last LSTM layer, a fully-connected layer, a softmax layer, and a classification layer are employed in order to detect the class labels of the input EEG signals. Various experiments were conducted with the MIT/BIH Polysomnographic Dataset. The experiments showed that the concatenated ResNet features achieved an accuracy score of 97.92%. The obtained accuracy score was also compared with the state-of-the-art scores and, to the best of our knowledge, the proposed method achieved the best accuracy score among the methods compared.Öğe Electrocardiogram beat classification using deep convolutional neural network techniques(Institute of Physics Publishing, 2020) Cömert, Zafer; Akbulut, Yaman; Akpınar, Muhammed H.; Alçin, Ömer Faruk; Budak, Ümit; Aslan, Muzaffer; Şengür, AbdulkadirThe electrocardiogram (ECG) is a useful method which enables the monitoring of various cardiac conditions, such as arrhythmia and heart rate variability (HRV). ECG beats help to determine various heart failures such as cardiac disease and ventricular tachyarrhythmia. In the literature, it can be seen that various advanced signal processing and machine learning techniques and deep learning algorithms have been employed for ECG beat categorization. These methods were generally based on either the time domain or frequency domain. Time-frequency (T-F) based techniques have also been proposed for ECG beat classification. In this chapter, a different model is proposed for the ECG beat classification task. In the proposed approach, the ECG beats are initially represented by images. Instead of using a time-frequency approach for converting the ECG beats to ECG images, we opt to use the ECG beats directly to construct the ECG images. In other words, the ECG beat values are directly saved as ECG images. Three deep convolutional neural network (CNN) approaches are considered in ECG beat classification. These approaches ensure end-to-end learning schema, fine-tuning of pre-trained CNN models, extraction of deep features and their classification using a traditional classifier, such as the support vector machine (SVM) or deep machine learning approaches. The well-known MIT-BIH arrhythmia database is considered in the evaluation of the proposed deep learning approaches. The database is separated into two sets, the training and test dataset in proportions of 75% and 25%, respectively. The experimental results are evaluated using the classification accuracy score. The results show that the proposed methods have potential for use in ECG beat classification.Öğe EMG Sinyalleri Kullanılarak GoogLeNet ve Çok Seviyeli DPD ile El Tutma Hareketlerinin Sınıflandırılması(Fırat University, 2022) Özküçük, Muhammed Buğracan; Alçin, Ömer Faruk; Gençoğlu, Muhsin TunayElektromiyografi (EMG) elektriksel aktiviteyi ölçmek için kullanılan bir yöntemdir. Bu yöntem günümüzde hastalık tespitinde kullanılmasıyla yaygınlaşmış olsa da robotik, protez kontrolü, video oyunları gibi popüler alanlarda yer edinmiştir. Bu çalışmada altı temel el hareketinin EMG sinyalleri kullanılarak sınıflandırılması amaçlanmıştır. Bu amaç doğrultusunda transfer öğrenme yaklaşımı kullanılmıştır. EMG sinyalleri çok seviyeli dalgacık paket dönüşümü (DPD) ile zaman-frekans (ZF) görüntülerine çevrilmiştir. Bütün kanallara ait ZF görüntülerinin %80’i birleştirilerek GoogLeNet mimarisini eğitmek için kullanılmıştır. Hareket tanımada başarımı artırmak için GoogLeNet’ten elde edilen öznitelikler Destek Vektör Makinesi (DVM) ile sınıflandırılmıştır. Önerilen yöntem altı temel el hareketini tanımada %98.833 doğruluk oranına sahiptir. Önerilen yöntem aynı veri setini kullanan yöntemler ile karşılaştırılmıştır. Yapılan karşılaştırmalar sonucunda önerilen yöntemin mevcut yöntemlerden %0.8 daha yüksek performans sergilediği görülmüştür. Deneysel çalışmalar önerilen yaklaşımın EMG ile hareket tanımada kullanılabilecek etkin ve verimli bir yöntem olduğunu göstermiştirÖğe Fraktal Eğimden Arındırılmış Dalgalılık Analizi ve Pencereli Kare Ortalamanın Karekökü Tabanlı EMG Sınıflandırma(Fırat Üniversitesi, 2020) Alçin, Ömer FarukElektromiyografik (EMG) kas aktivitesini ölçmek için kullanılan faydalı bir tekniktir. EMG sinyalleri çoğunlukla protez, fiziksel rehabilitasyon, Nöromusküler bozuklukların teşhisi ve beyin-bilgisayar arayüzü gibi medikal uygulamalara yardımcı karar destek sistemlerinde kullanılır. Bu çalışmada EMG sinyallerini sınıflamak için kullanışlı bir yaklaşım önerilmiştir. Önerilen yöntemde, özellik çıkarma yöntemi olarak Fraktal Eğimden Arındırılmış Dalgalanma Analizi (F-EADA) ve örtüşmeyen pencereli Kök Ortalama Karesi (p-KOK) kullanılmıştır. F-EADA yöntemi korelasyon ve istatistiksel benzerliği ölçmek için kullanılan bir yöntemdir. KOK istatistiksel bir ölçüdür ve EMG tanıma sistemlerinde ayırt edici bir parametre olabilmektedir. p-KOK yaklaşımı geleneksel KOK yönteminden daha yeteneklidir ve bu ayırt edici yetenek deneysel sonuçlarla gösterilmiştir. Çıkarılan EMG öznitelikleri, Destek Vektör Makinesi (DVM), k-En Yakın Komşu (kEYK), Karar Ağacı (KA) ve Doğrusal Diskriminant Analizi (DDA) yöntemleri ile sınıflandırılmıştır. DVM Sınıflandırıcı bu yöntemler arasında en iyi performansa sahiptir. Önerilen yöntem, altı farklı nesne tutma eylemini içeren EMG veri seti ile test edilmiştir. Deneysel çalışmalar, önerilen yöntemin %96.83 doğruluk ile EMG veri setini sınıflamak için uygun olduğunu göstermiştir. Ayrıca, önerilen yöntem aynı veri setini kullanan diğer yöntemlerle karşılaştırıldığında daha iyi performansa sahiptirÖğe A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals(IEEE (Institute of Electrical and Electronics Engineers), 2020) Siuly, Siuly; Alçin, Ömer Faruk; Kabir, Enamul; Şengür, Abdulkadir; Wang, Hua; Zhang, Yanchun; Whittaker, FrankMild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.Öğe A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings(Institute of Electrical and Electronics Engineers Inc., 2022) Sobahi, Nebras; Çakar, Hakan; Arı, Berna; Alçin, Ömer Faruk; Şengür, AbdulkadirMachine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods. IEEEÖğe Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors(Wolters Kluwer Health, 2022) Gündüz, Emrah; Alçin, Ömer Faruk; Kızılay, Ahmet; Piazza, CesareAll studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.Öğe Transfer Öğrenme Yaklaşımı Kullanılarak İzolatör Kusurlarının Tespiti(2024) ÖZKÜÇÜK, Muhammed Buğracan; Alçin, Ömer Faruk; GENÇOĞLU, Muhsin TunayElektrik enerjisinin iletimi ve dağıtımı, modern toplumların işleyişinde hayati bir rol oynamaktadır. Bu enerjinin güvenli ve kesintisiz bir şekilde taşınması, elektrik sistemlerinin sağlıklı bir şekilde çalışmasıyla mümkün olmaktadır. Ancak, elektrik iletim hatlarındaki kusurlar, sistemde arızalara ve enerji kesintilerine neden olabilmektedir. İzolatör kusurları, elektrik hatlarındaki en yaygın arızalar arasında yer almaktadır. Bu kusurlar, genellikle izolatör yüzeyindeki çatlaklar, kırıklar, erozyon veya kimyasal bozulmalar şeklinde ortaya çıkmaktadır. Son yıllarda, yapay zeka ve makine öğrenmesi teknikleri, izolatör kusurlarının belirlenmesi için alternatif bir çözüm sunmuştur. Bu alanda transfer öğrenme, özellikle dikkat çeken bir yaklaşım olarak ön plana çıkmaktadır. Bu yaklaşım, izolatör kusurlarının tespitinde kullanılan verilerden öğrenilen bilgilerin, yeni bir izolatördeki kusurların belirlenmesinde kullanılmasına olanak sağlamaktadır. Bu çalışmada izolatör görüntülerinden transfer öğrenme yaklaşımı kullanılarak izolatör türü ve sağlamlık durumu (normal/kusurlu) tespiti yapılmıştır. Bu problemlerin verimli çözümü için Çoklu Öğrenme yaklaşımı dikkate alınmıştır. Bu durumlar literatürde yaygın olarak kullanılan çok sınıflı görüntü veri setlerinde iyi başarımlar gösteren AlexNet, ResNet50 ve GoogLeNet gibi mimarilere giriş olarak uygulanmıştır. İzolatörün sağlamlık durumunun tespitinde en iyi doğruluk oranına % 97.674 ile AlexNet ve ResNe50 mimarilerinde ulaşılmıştır. İzolatör türünün belirlenmesinde en iyi doğruluk oranına % 90.698 ile ResNe50 mimarisinde ulaşılmıştır.Öğe Two-stepped majority voting for efficient EEG-based emotion classification(Springer, 2020) Ismael, Aras Masood; Alçin, Ömer Faruk; Abdalla, Karmand Hussein; Şengür, AbdulkadirIn this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.
