Sleep stage classification based on filter bank optimization

Çodur, Muhammet Mustafa
Sleep stage binary classification is studied using single channel EEG signals. The proposed approach is composed of two steps. In the first step, cepstrum coefficients based features are obtained from EEC signals using a filter bank approach which is tuned for sleep stage classification in terms of number of filters and their type. In the second step, these features are used with support vector machine approach for classification. It is observed that obtained results are comparable with the published results, and therefore, it is promising.


Generalized filter bank design for sleep stage classification
ORAL, EMİN ARGUN; Çodur, Muhammet Mustafa; ÖZBEK, İBRAHİM YÜCEL (2017-11-02)
In this study, binary sleep stage classification (sleep or awake state) was performed using single-channel EEG signal. A new frequency warping function is proposed for this purpose. This function provides a bending function that can proper orientation and depth of the EEG signal frequency content. In this way a generalized filter set of was designed. With the help of this filter set, cepstrum features are extracted. In classification stage, Support Vector Machines (SVM) are employed because of its good perf...
Cepstrum coefficients based sleep stage classification
Oral, Emin Argun; Çodur, Muhammet Mustafa; Özbek, İbrahim Yücel (null; 2017-12-12)
This paper examines filterbank parameters to extract discriminative cepstrum coefficient from EEG signals for sleep stage classification using well-known Support Vector Machine (SVM) algorithm. The proposed method has three main stages as feature extraction, training and classification. In feature extraction step, features are obtained using linear frequency cepstrum coefficients (LFCC) of EEG signals. Then SVM classifier is trained based on the extracted features. In the final step of classification, the c...
Computational analysis of network activity and spatial reach of sharp wave-ripples
Canakci, Sadullah; Toy, Muhammed Faruk; Inci, Ahmet Fatih; Liu, Xin; Kuzum, Duygu (Public Library of Science (PLoS), 2017-9-15)
Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses fro...
Gender clasification based on single channel EEG signal
ORAL, EMİN ARGUN; ÖZBEK, İBRAHİM YÜCEL; Çodur, Muhammet Mustafa (2017-11-02)
This paper presents an approach for gender recognition from single channel EEG signal. For this purpose, approximately 24 hour-long EEG data, obtained during daily routine activities including sleep, was used. First, cepstrum coefficients of EEG signals were obtained in the frequency domain to construct the features SET. Second, a machine learning step was performed using these features with Support Vector Machines (SVM). Finally, gender identification was performed on the test data for which features were ...
Investigation of music algorithm based and wd-pca method based electromagnetic target classification techniques for their noise performances
Ergin, Emre; Sayan, Gönül; Department of Electrical and Electronics Engineering (2009)
Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries con...
Citation Formats
E. A. ORAL, M. M. Çodur, and İ. Y. ÖZBEK, “Sleep stage classification based on filter bank optimization,” 2017, Accessed: 00, 2020. [Online]. Available: