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Feature Selection and Classification of EEG Finger Movement Based on Genetic Algorithm
(Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018, 2018)
Electroencephalography (EEG) classification for mental tasks is the crucial part of the braincomputer interface. Many studies try to extract discriminative features from EEG signals. In this study, feature selection algorithm ...
Feature selection for ECG beat classification using genetic algorithms with a multi-objective approach [Çok Amaçli Yaklasimla Genetik Algoritmalar Kullanarak EKG Vuru Siniflandirmasi için Öznitelik Seçimi]
(26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, 2018)
To identify appropriate features in classification studies is a common problem in many areas. In this study, a genetic algorithm method with multi-objective approach is proposed for selecting the features that give high ...
Analysis and classification of air disc brake sounds in time and frequency domain [Zaman ve Siklik Ortaminda Havali Disk Fren Seslerinin Incelenmesi ve Siniflandirilmasi]
(26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, 2018)
In this study, analysis and classification of audio data collected from faulty air disc brakes has been carried out by Fourier Transform (FT). The sound data have been recorded by 2 identical Norsonic Type 1228 microphones ...
Multiwavelet feature sets for ECG beat classification [EKG Vuru Siniflandirmasi için Çoklu Dalgacik Öznitelik Setleri]
(2017 25th Signal Processing and Communications Applications Conference, SIU 2017, 2017)
In this study, heart beats are classified as normal, right branch block, left branch block, and paced rhythm using electro cardiographic (ECG) signals obtained from the MIT-BIH cardiac arrhythmia database. Average, standard ...
The comparison of LMS based algorithms for active cancellation of motor noise [Motor gürültüsünün aktif bastirimi için LMS tabanli algoritmalarin karsilastirilmasi]
(2013 21st Signal Processing and Communications Applications Conference, SIU 2013, 2013)
In this paper, an active noise cancellation system to decrease the motor noise arriving to car driver is proposed. This system can be considered as a feedforward control system since it enables taking motor noise as the ...
The calculation of target poles with wavelet transform for electromagnetic target discrimination [Elektromanyeti̇k hedef ayirtetme i̇çi̇n dalgacik dönüşümü i̇le hedef kutuplarinin elde edi̇lmesi̇]
(2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings, 2012)
In this paper, a method oriented to the extraction of target poles is presented for target recognition purpose. By applying wavelet transform to whole signal, the mention method obtains target poles with a map extracted ...
Segmentation of multiple sclerosis plagues by robust fuzzy clustering with spatial information
(INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications, 2011)
In this study, a fuzzy clustering method has been proposed in order to segment brain tissues affected by the multiple sclerosis (MS). In traditional fuzzy clustering, the neighboring relations between pixels have not been ...
Comparison of wavelet based feature extraction methods for speech/music discrimination
(Istanbul University - Journal of Electrical and Electronics Engineering, 2011)
The speech/music discrimination systems have gaining importance in several intelligent audio retrieval algorithms due to the increasing size of the multimedia sources in our daily lives. This study aims to propose a ...
Discrete and dual tree wavelet features for real-time speech/music discrimination
(ISRN Signal Processing, 2011)
The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been ...
ECG Beat Arrhythmia Classification by using 1-D CNN in case of Class Imbalance
(IEEE, 2019)
In this study, ECG arrhythmia types of non-ectopic (N), ventricular ectopic (V), unknown (Q), supraventricular ectopic (S) and fusion (F) were classified by using the convolutional neural network (CNN) architecture. QRS ...
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