Browsing by Author "Devecioglu, Ozer Can"

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Citation - WoS: 29
Citation - Scopus: 37
Blind Ecg Restoration by Operational Cycle-Gans
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Kiranyaz, Serkan; Devecioglu, Ozer Can; İnce, Türker; Malik, Junaid; Chowdhury, Muhammad; Hamid, Tahir; Mazhar, Rashid
Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve.
Citation - WoS: 24
Citation - Scopus: 33
Early Bearing Fault Diagnosis of Rotating Machinery by 1d Self-Organized Operational Neural Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) İnce, Türker; Malik, Junaid; Devecioglu, Ozer Can; Kiranyaz, Serkan; Avcı, Onur; Eren, Levent; Gabbouj, Moncef
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of them addressed bearing fault severity classification for early fault diagnosis with high enough accuracy. 1D Convolutional Neural Networks (CNNs) have indeed achieved good performance for detecting RM bearing faults from raw vibration and current signals but did not classify fault severity. Furthermore, recent studies have demonstrated the limitation in terms of learning capability of conventional CNNs attributed to the basic underlying linear neuron model. Recently, Operational Neural Networks (ONNs) were proposed to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, we propose 1D Self-organized ONNs (Self-ONNs) with generative neurons for bearing fault severity classification and providing continuous condition monitoring. Experimental results over the benchmark NSF/IMS bearing vibration dataset using both x- and y-axis vibration signals for inner race and rolling element faults demonstrate that the proposed 1D Self-ONNs achieve significant performance gap against the state-of-the-art (1D CNNs) with similar computational complexity.
Citation - WoS: 53
Citation - Scopus: 65
Enhanced Bearing Fault Detection Using Multichannel, Multilevel 1d Cnn Classifier
(Springer, 2022) Ozcan, Ibrahim Halil; Devecioglu, Ozer Can; İnce, Türker; Eren, Levent; Askar, Murat
Electric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Through early detection of bearing faults, it would be possible to solve the problem at a lower cost by repairing and/or replacing relevant parts. Most of the fault detection works in the literature attempted to detect binary {healthy, faulty} motor fault case based on a single input. In this study, we propose an enhanced performance bearing fault diagnosis system based on multichannel, multilevel 1D-CNN classifier processing vibration data collected from multiple accelerometers mounted on bearings in a test bed. Effectiveness and feasibility of the proposed method are validated by applying it to the benchmark IMS bearing vibration dataset for inner race and rolling element faults and comparing the results with the conventional single-axis data-based fault detection.
Citation - Scopus: 1
Improved Detection of Broken Rotor Bars by 1-D Self-Onns
(IEEE, 2022) Eren, Levent; Devecioglu, Ozer Can; Ince, Turker; Askar, Murat
Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.
Citation - Scopus: 7
Improved Domain Adaptation Approach for Bearing Fault Diagnosis
(IEEE, 2022) Ince, Turker; Kilickaya, Sertac; Eren, Levent; Devecioglu, Ozer Can; Kiranyaz, Serkan; Gabbouj, Moncef
Application of domain adaptation techniques to predictive maintenance of modern electric rotating machinery (RM) has significant potential with the goal of transferring or adaptation of a fault diagnosis model developed for one machine to be generalized on new machines and/or new working conditions. The generalized nonlinear extension of conventional convolutional neural networks (CNNs), the self-organized operational neural networks (Self-ONNs) are known to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, first the state-of-the-art 1D CNNs and Self-ONNs are tested for cross-domain performance. Then, we propose to utilize Self-ONNs as feature extractor in the well-known domain-adversarial neural networks (DANN) to enhance its domain adaptation performance. Experimental results over the benchmark Case Western Reserve University (CWRU) real vibration data set for bearing fault diagnosis across different load domains demonstrate the effectiveness and feasibility of the proposed domain adaptation approach with similar computational complexity.
Citation - WoS: 7
Citation - Scopus: 10
Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma Using Color Fundus Photography
(Turkish Ophthalmological Soc, 2022) Atalay, Eray; Ozalp, Onur; Devecioglu, Ozer Can; Erdogan, Hakika; İnce, Türker; Yildirim, Nilgun
Objectives: To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes. Materials and Methods: A total of 9,950 fundus photographs of 5,388 patients from the database of Eskisehir Osmangazi University Faculty of Medicine Ophthalmology Clinic were labelled as glaucoma, glaucoma suspect, or normal by three different experienced ophthalmologists. The categorized fundus photographs were evaluated using a state-of-the-art two-dimensional CNN and compared with deep residual networks (ResNet) and very deep neural networks (VGG). The accuracy, sensitivity, and specificity of glaucoma detection with the different algorithms were evaluated using a dataset of 238 normal and 320 glaucomatous fundus photographs. For the detection of suspected glaucoma, ResNet-101 architectures were tested with a data set of 170 normal, 170 glaucoma, and 167 glaucoma-suspect fundus photographs. Results: Accuracy, sensitivity, and specificity in detecting glaucoma were 96.2%, 99.5%, and 93.7% with ResNet-50; 97.4 degrees A, 97.8%, and 97.1% with ResNet-101; 98.9%, 100%, and 98.1% with VGG-19, and 99.4%, 100%, and 99% with the 2D CNN, respectively. Accuracy, sensitivity, and specificity values in distinguishing glaucoma suspects from normal eyes were 62%, 68%, and 56% and those for differentiating glaucoma from suspected glaucoma were 92%, 81%, and 97%, respectively. While 55 photographs could be evaluated in 2 seconds with CNN, a clinician spent an average of 24.2 seconds to evaluate a single photograph. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.
Citation - WoS: 1
Citation - Scopus: 3
Patient-Specific Imaginary Motor Movement Classification of Eeg Signals and Control of Robotic Arm
(IEEE, 2019) Devecioglu, Ozer Can; Yaman, Burak; Mesekoparan, Ozle; Cakir, Can; İnce, Türker
This paper presents development of a robotic arm that can mimic the mobility of human arm with the recorded EEG (electroencephalogram) signals from the brain. This is a project designed for people who have lost the ability to control their arms. The robot arm will work with the signals that will be produced by the EEG instrument simultaneously when the user thinks that he or she will perform certain movements. These movements can be classified using Deep Multilayer Perceptron (Deep MLP) classifier designed in Python with Keras library. Deep MLP network is trained using Backpropagation algorithm for each patient by using own data. Experimental results demonstrate the proposed method can classify different imaginary movements with high accuracy.
Citation - WoS: 39
Citation - Scopus: 63
Real-Time Glaucoma Detection From Digital Fundus Images Using Self-Onns
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Devecioglu, Ozer Can; Malik, Junaid; İnce, Türker; Kiranyaz, Serkan; Atalay, Eray; Gabbouj, Moncef
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.
Citation - WoS: 59
Citation - Scopus: 83
Real-Time Patient-Specific Ecg Classification by 1d Self-Operational Neural Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Malik, Junaid; Devecioglu, Ozer Can; Kiranyaz, Serkan; İnce, Türker; Gabbouj, Moncef
Objective: Despitethe proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Methods: Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. Results: Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported. Conclusion: As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity.
Citation - WoS: 54
Citation - Scopus: 75
Robust R-Peak Detection in Low-Quality Holter Ecgs Using 1d Convolutional Neural Network
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Zahid, Muhammad Uzair; Kiranyaz, Serkan; İnce, Türker; Devecioglu, Ozer Can; Chowdhury, Muhammad E. H.; Khandakar, Amith; Tahir, Anas
Objective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Methods: In this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R-peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. Results: The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision. Significance: Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. Conclusion: Finally, the simple and invariant nature of the parameters leads to a highly generic system and therefore applicable to any ECG dataset.