Elektrik ve Enerji Bölümü

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Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(2021) Yildiz, Abdulnasir; Zan, Hasan; Said, Sherif; Zan, Hasan
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(Biomedical Signal Processing and Control, 2021) Zan, Hasan; Yıldız, Abdulnasir; Said, Sherif; Zan, Hasan
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Harmonics Mitigation of Stand‑Alone Photovoltaic System Using LC Passive Filter
(Journal of Electrical Engineering & Technology, 2021) Adak, Süleyman
This article investigates modeling and simulation of the of-grid photovoltaic (PV) system, and elimination of harmonic components using an LC passive flter. Pulse width modulation (PWM) inverter is used to convert the direct current to alternating current. It is very important in terms of energy quality that the inverter output current total harmonic distortion (THDI) is below the value given by standards. Harmonic components have negatively efect on of-grid PV power system. THDI should be kept below a certain level in order to prevent damage to the equipment in the of-grid system and to ensure a higher quality energy fow to reduce the total harmonic distortion (THD) of the solar inverter output current; LC passive flter must be connected to the output of the PWM inverter. There are many types of passive flters for solar inverters. One of the most widely used flter types is the LC flter. LC flters are used in of-grid systems. LC flter is smaller in size and lower cost than other flters. But it is more complicated to determine the parameters of the LC flter. Therefore, in order for the system to remain in a steady state, the parameters must be accurately calculated and analyzed. In this study, the output power of the solar inverter, switching frequency, bus voltage etc. values were determined and LC flter parameters were calculated. Since high inductance values are used in LC flters, the voltage drop increases in these flters. To reduce the voltage drop, the DC bus voltage must be increased, which increases the switching losses. LC flter is connected between the inverter and the nonlinear load to flter the harmonic components produced by the DC/DC boost converter, DC/AC inverter and non-linear load. Matlab/Simulink program was used in Simulation and analysis of of-grid solar system. Solar inverter output current THD was measured as 91.55%. After the LC flter is connected to the system, this value has dropped to 2.62%.
Harmonics Mitigation of Stand-Alone Photovoltaic System Using LC Passive Filter
(Journal of Electrical Engineering and Technology, 2021) Adak, Süleyman
This article investigates modeling and simulation of the off-grid photovoltaic (PV) system, and elimination of harmonic components using an LC passive filter. Pulse width modulation (PWM) inverter is used to convert the direct current to alternating current. It is very important in terms of energy quality that the inverter output current total harmonic distortion (THDI) is below the value given by standards. Harmonic components have negatively effect on off-grid PV power system. THDI should be kept below a certain level in order to prevent damage to the equipment in the off-grid system and to ensure a higher quality energy flow to reduce the total harmonic distortion (THD) of the solar inverter output current; LC passive filter must be connected to the output of the PWM inverter. There are many types of passive filters for solar inverters. One of the most widely used filter types is the LC filter. LC filters are used in off-grid systems. LC filter is smaller in size and lower cost than other filters. But it is more complicated to determine the parameters of the LC filter. Therefore, in order for the system to remain in a steady state, the parameters must be accurately calculated and analyzed. In this study, the output power of the solar inverter, switching frequency, bus voltage etc. values were determined and LC filter parameters were calculated. Since high inductance values are used in LC filters, the voltage drop increases in these filters. To reduce the voltage drop, the DC bus voltage must be increased, which increases the switching losses. LC filter is connected between the inverter and the nonlinear load to filter the harmonic components produced by the DC/DC boost converter, DC/AC inverter and non-linear load. Matlab/Simulink program was used in Simulation and analysis of off-grid solar system. Solar inverter output current THD was measured as 91.55%. After the LC filter is connected to the system, this value has dropped to 2.62%.
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(2023) Zan, Hasan; Yıldız, Abdulnasır; Zan, Hasan
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen's kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-of-the-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(ScienceDirect, 2023) Zan, Hasan; Yildiz, Abdulnasır; Zan, Hasan
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen’s kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-ofthe-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(IOP Publishing, 2023) Zan, Hasan; Yıldız, Abdulnasir; Zan, Hasan
Objective: Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach: In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results: By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance: Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(2023) Zan, Hasan; Yıldız, Abdulnasır; Zan, Hasan
Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results. By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network
(IEEE, 2021) Zan, Hasan; Yıldız, Abdulnasır; Zan, Hasan
Sleep arousal is defined as a shift from deep sleep to light sleep or complete awakening. Arousals cause sleep deprivation by fragmenting sleep, and ultimately, many health problems. Arousals can be induced by well-studied apneas and hypopneas or other sleep orders such as hypoventilation, bruxism, respiratory effort-related arousals. Thus, detection of less-studied non-apnea/hypopnea arousals is important for diagnosis and treatment of sleep disorders. Traditionally, polysomnography (PSG) test that is recording and inspecting overnight physiological signals is used for sleep studies. In this work, a novel method based on one dimensional local binary pattern (1D-LBP) and convolutional neural network (CNN) for automatic arousal detection from polysomnography recordings is proposed. 25 recordings from PhysioNet Challenge 2018 PSG dataset are used for experiments. Each signal in PSG recordings is transformed to a new signal using 1D-LBP, and then segmented using 10-s-long sliding window. The segments are fed to a CNN model formed by stacking 25 layers for classification of non-apnea/hypopnea arousal regions from non-arousal regions. Area under precision-recall curve (AUPRC) and area under receiver operating characteristic curve (AUROC) metrics are used for performance measurement. Experimental results reflect that the proposed method shows a great promise and obtains an AUPRC of 0.934 and an AUROC of 0.866.
Thevenin Equivalent of Solar PV Cell Model and Maximum Power Transfer
(IEEE, 2021) Adak, Süleyman; Cangi, Hasan; Yılmaz, A. Serdar
Photovoltaic (PV) is the conversion of solar energy into DC electrical energy using PV cells. In addition, solar energy is an important renewable energy source. In this study, it is proposed that Thevenin's equivalent PV cell model produces a voltage-current characteristic that is quite representative of the operation of the PV source. Thevenin's elements depend on ambient temperature conditions, so charging is derived and simplified to construct a model that closely predicts and demonstrates adequate PV cell characteristic for different ambient temperature conditions. This method is very useful for estimating the desired performance and also for examining different Maximum Power Point Tracking (MPPT) algorithms. Theoretically, the simulation was supplemented with test data, then used to develop an equivalent Thevenin model in which the resistance is non-linear and voltage dependent. Thevenin's method and variable pitch is to improve the maximum power transfer to the load by increasing the performance of the PV cell. These methods were modeled and studied in a simulation program.

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