Browsing by Author "Guren O."

Now showing 1 - 5 of 5

Citation - WoS: 9
Citation - Scopus: 17
Abnormal Ecg Beat Detection Based on Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2020) Ozdemir M.A.; Guren O.; Cura O.K.; Akan A.; Onan A.; Ozdemir, Mehmet Akif; Guren, Onan; Cura, Ozlem Karabiber; Akan, Aydin; Onan, Aytug
The heart is the most critical organ for the sustainability of life. Arrhythmia is any irregularity of heart rate that causes an abnormality in your heart rhythm. Clinical analysis of Electrocardiogram (ECG) signals is not enough to quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning method for the accurate detection of abnormal and normal heartbeats based on 2-D Convolutional Neural Network (CNN) architecture. Two channels of ECG signals were obtained from the MIT-BIH arrhythmia dataset. Each ECG signal is segmented into heartbeats, and each heartbeat is transformed into a 2-D grayscale heartbeat image as an input for CNN structure. Due to the success of image recognition, CNN architecture is utilized for binary classification of the 2-D image matrix. In this study, the effect of different CNN architectures is compared based on the classification rate. The accuracies of training and test data are found as 100.00% and 99.10%, respectively for the best CNN model. Experimental results demonstrate that CNN with ECG image representation yields the highest success rate for the binary classification of ECG beats compared to the traditional machine learning methods, and one-dimensional deep learning classifiers. © 2020 IEEE.
Comparison of Parallel Magnetic Resonance Imaging Algorithms: Pils and Sense
(Institute of Electrical and Electronics Engineers Inc., 2020) Dorum E.; Unay M.; Guren O.; Akan A.; Unay, Mazlum; Dorum, Egehan; Guren, Onan; Akan, Aydin
Magnetic Resonance (MR) imaging has always followed a developmental path by incorporating new algorithms in terms of image quality and imaging duration. In MR imaging performed in hospitals and clinics, the duration of imaging is an important consideration in terms of both for the comfort of the patient and the number of patients who can be taken daily. One of the approaches to shorten the imaging time is the parallel imaging method. After parallel imaging algorithms started being used, imaging duration up to 1 hour with traditional methods has been reduced to minutes or even seconds depending on the number of receiving coils and the type of algorithm used. In this paper; comparison of the widely used parallel imaging algorithms such as Partially Parallel Imaging With Localized Sensitivities (PILS), and Sensitivity Encoding (SENSE) and evaluation of advantages and disadvantages of these algorithms over each other were performed utilizing the numerical calculation software named MATLAB. © 2020 IEEE.
Citation - WoS: 12
Citation - Scopus: 17
Emg Based Hand Gesture Classification Using Empirical Mode Decomposition Time-Series and Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2020) Kisa D.H.; Ozdemir M.A.; Guren O.; Akan A.; Ozdemir, Mehmet Akif; Kisa, Deniz Hande; Guren, Onan; Akan, Aydin
Computer systems working with artificial intelligence can recognize movements and gestures to be used for many purposes. In order to perform recognition, the electrical activity of the muscles can be utilized which is represented by electromyography (EMG) and EMG is not a stationary biological signal. EMG based movement recognition systems have an important place in distinct areas like in human-computer interactions, virtual reality, prosthesis, and hand exoskeletons. In this study, a new approach based on deep learning (DL) and Empirical Mode Decomposition (EMD) is proposed to improve the accuracy rate for recognition of hand movements in its application areas. Firstly, 4-channel surface EMG (sEMG) signals were measured while simulating 7 different hand gestures, which are extension, flexion, ulnar deviation, radial deviation, punch, open hand, and rest, from 30 subjects. After that, noiseless signals were procured utilizing filters as a result of preprocessing. Then, pre-processed signals were subjected to segmentation. Thereafter, the EMD process was applied to each segmented signal and Intrinsic Mode Functions (IMFs) were obtained. The IMFs time-series which are some kind of screen images of the first 3 IMFs have been recorded. For classification, IMFs images have given as inputs and have trained to the 101layer Convolution Neural Network (CNN) based on Residual Networks (ResNet) architecture, which is a DL model. © 2020 IEEE.
Citation - WoS: 38
Citation - Scopus: 54
Emg Based Hand Gesture Recognition Using Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2020) Ozdemir M.A.; Kisa D.H.; Guren O.; Onan A.; Akan A.; Ozdemir, Mehmet Akif; Kisa, Deniz Hande; Guren, Onan; Onan, Aytug; Akan, Aydin
The Electromyography (EMG) signal is a nonstationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and amputee device controls. In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 participants. The obtained sEMG signals have been segmented into sections where each movement was found. Then, spectrogram images of the segmented sEMG signals have been created by means of ShortTime Fourier Transform (STFT). The created colored spectrogram images have trained with 50-layer Convolutional Neural Network (CNN) based on Residual Networks (ResNet) architecture. Owing to the proposed method, test accuracy of 99.59% and F1 Score of 99.57% have achieved for 7 different hand gesture classifications. © 2020 IEEE.
Citation - Scopus: 3
Estimation of Emotion Status Using Iaps Image Data Set
(Institute of Electrical and Electronics Engineers Inc., 2020) Yesilkaya B.; Guren O.; Bahar M.T.; Turhal L.N.; Akan A.; Bahar, Mehmet Tugay; Turhal, Leyla Nur; Yesilkaya, Bartu; Guren, Onan; Akan, Aydin
Emotion recognition is an effective analysis method used to increase the interaction between human-machine interface. EEG based emotion recognition studies based on brain signals are preferred in order to provide healthy results of emotion analysis experiments. In this study, emotion recognition analysis was performed in accordance with dimensional emotion modelling. Data cleaning was performed by applying the necessary filters on the recorded data. The feature vector was then created and the success rate was determined using support vector machines and classification methods such as K-nearest neighbour. © 2020 IEEE.