Detection of Sleep Spindles in Sleep EEG by using the PSD Methods

Yücelbaş, Cüneyt; Yücelbaş, Şule; Özşen, Seral; Tezel, Gülay; Küççüktürk, Serkan; Yosunkaya, Şebnem

doi:10.17485/ijst/2016/v9i25/96628

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…The feature set was created by LLL, LLH, LHL, LHH, HLL, HLH, HHL, and HHH coefficients after 3D-DWT. The mean, variance, kurtosis, skewness, entropy, and energy (Equations 13-18) of features [15,16] were calculated from the coefficients.…”

Section: 𝑤𝑤 𝜑mentioning

confidence: 99%

Classification of 3D-DWT Features of Brain Tumours with SVM

Barstuğan

2023

OPRD

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Brain tumours are one of the most challenging medical conditions to diagnose and treat. Accurate and timely classification of brain tumours is critical for effective treatment planning and patient management. Machine learning algorithms have shown great promise in improving the accuracy of brain tumour classification. This study implemented high-grade glioma (HGG) and low-grade glioma (LGG) classification on four different 3D-MRI (magnetic resonance imaging) scans (FLAIR, T1, T1c, T2). By using four different scans, 15 different combinations were created for classification process. 3D Discrete Wavelet Transform was used to transform tumour images for feature extraction stage. 36 different wavelet types were used for image transformation. First Order Statistics (mean, variance, kurtosis, skewness, entropy, energy) were extracted from transformed images of 36 wavelet types. Support Vector Machines (SVM) algorithm classified the FOS features that were obtained on BraTS 2017 dataset. The 2-fold, 5-fold, and 10-fold cross-validations are implemented and six metrics (sensitivity, specificity, accuracy, precision, F1-score, AUC) evaluated the performance of proposed method. Consequently, proposed method achieved remarkable scores of 95.23% (sensitivity), 78.81% (specificity), 90.89% (accuracy), 92.59% (precision), 93.89% (F1-score), and 87.02% (AUC) for HGG/LGG classification of 3D brain MRI data on T1+T1c+T2 combination by 2-fold cross validation.

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Section: 𝑤𝑤 𝜑mentioning

confidence: 99%

Classification of 3D-DWT Features of Brain Tumours with SVM

Barstuğan

2023

OPRD

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“…The 'Confusion Matrix' is the basis for these statistical evaluations. Many statistical values such as Cohen's Kappa coefficient (Kappa) and classification accuracy rate (ACC) can be calculated according to this matrix formed by the number of true and false labeling [33][34][35][36].…”

Section: Detection Systems and Statistical Evaluationmentioning

confidence: 99%

“…The fact that this value is close to '1' indicates that there is an excellent separation between the data of different classes. Lastly, ACC results were calculated as follow by equation ( 9) and all metrics were evaluated [33,34]. Errors of detection systems were plotted additionally.…”

Section: Detection Systems and Statistical Evaluationmentioning

confidence: 99%

Automatic detection of coagulation and carbonization in laser applications using machine learning techniques

Yücelbaş¹

2020

Laser Phys.

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Laser-induced thermal treatment (LITT) technique is considered to be the most effective method for minimally invasive surgical procedures. However, the parameters of laser systems used in surgical operations need to be adjusted very precisely. Otherwise, irreversible situations may occur especially in the operation of sensitive organs such as the brain. In order to avoid such undesirable situations, artificial intelligence algorithms are used in the fields of engineering, aerospace and automotive, especially in medicine and biomedical. In this study, it was aimed to automatically detect Coagulation (Co) and Carbonization (Ca) states using a total of 140 data obtained as a result of LITT experimental applications (1070 nm and seven different power density values) on ex vivo liver tissue. In the first part of the study, which consists of two main stages, the data set was presented to seven different expert systems based on artificial intelligence (C4.5, KNN, ANN, RF, MLR, SMO-SVM and LWLDS), and automatically Co and Ca formations were determined. Among the proposed expert systems, the C4.5 algorithm showed the best performance with 100% accuracy and 0% error rate. In the second stage of the study, the same data set was given to Attribute Information Gain (AIG) ranking system, which was used to determine the minimum number of feature that would provide the maximum performance, and the gain values of the attributes were calculated. As a result of this application, the most effective feature was found to be Normalized Depth of Thermal Damage (NDeTD) with 100% accuracy rate. Thus, the purpose of using the AIG system has been successfully achieved. As a result, thanks to the expert systems presented within the scope of the study, Co and Ca formations were determined automatically with 100% success rate according to the required parameters without laser application on ex vivo liver tissue. The fact that the aim of the study was realized with the highest success rate is thought to be the first in terms of contributing to the literature. Through this study, as a result of the laser treatment to be applied on cancerous tissue, it can be predicted whether the tissue will suffer irreversible damage and necessary precautions can be taken beforehand.

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“…Controlling the various devices with the programmable logic controller can be used only in the case of group power generation system. For a single person small controller circuit is required [24][25][26].…”

Section: Automatic Control Of Generated Energymentioning

confidence: 99%

Human, the Electric Machine

Pandey¹

2016

IJAAS

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In modern time we are using lot of electronic devices but to run an electronic unit power generation is taking place. Major issue is it also generating problem of pollution. So, an effort has been made to harvest energy from human body for power supply. In this paper different method of energy harvesting from human body such as thermo electric power, power from heartbeat, angular knee and footsteps motion are briefly reviewed and showed that how principle of thermo electric power may be helpful to reduce abnormal level of β brain wave, caused due excessive stress and/or anxiety. An automation system can be used to control these generated power. This paper also presents a ladder logic algorithm to control for human generated power using PLC programming, with RS-232 DF1interface.

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Detection of Sleep Spindles in Sleep EEG by using the PSD Methods

Cited by 11 publications

References 19 publications

Classification of 3D-DWT Features of Brain Tumours with SVM

Classification of 3D-DWT Features of Brain Tumours with SVM

Automatic detection of coagulation and carbonization in laser applications using machine learning techniques

Human, the Electric Machine

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