Generalized filter bank design for sleep stage classification

Çodur, Muhammet Mustafa
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 performance at binary classification. According to the experimental results, the highest correct classification rate(accuracy) is 98.40%. The result is better than studies which use same database in literature.


Sleep stage classification based on filter bank optimization
ORAL, EMİN ARGUN; Çodur, Muhammet Mustafa; ÖZBEK, İBRAHİM YÜCEL (2017-12-01)
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 result...
Automated detection of sleep spindles
Görür, Dilan; Gençer, Nevzat G.; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2003)
Sleep spindles are one of the rhythmic activities observed in sleep electroencephalogram (EEG). As they are well defined and functional, sleep spindle analysis is significant for brain research. Identifying the characteristics of sleep spindles may lead to an understanding of the functions of sleep. Furthermore, understanding the sleep spindle generation mechanisms can explain the other rhythmical activity occurring in other brain regions. The detection process of the sleep spindle data of a whole night sle...
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...
A Sparse Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-08-23)
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the voxel selection methods with the mesh learning method. We, first, select the "most discriminative" voxels usin...
A Parametric Estimation Approach to Instantaneous Spectral Imaging
Öktem, Sevinç Figen; Davila, Joseph M (2014-12-01)
Spectral imaging, the simultaneous imaging and spectroscopy of a radiating scene, is a fundamental diagnostic technique in the physical sciences with widespread application. Due to the intrinsic limitation of two-dimensional (2D) detectors in capturing inherently three-dimensional (3D) data, spectral imaging techniques conventionally rely on a spatial or spectral scanning process, which renders them unsuitable for dynamic scenes. In this paper, we present a nonscanning (instantaneous) spectral imaging techn...
Citation Formats
E. A. ORAL, M. M. Çodur, and İ. Y. ÖZBEK, “Generalized filter bank design for sleep stage classification,” 2017, Accessed: 00, 2020. [Online]. Available: