Browsing by Author "Asyali, Musa H."

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Citation - WoS: 47
Citation - Scopus: 57
Applications of parametric spectral estimation methods on detection of power system harmonics
(Elsevier Science SA, 2008) Ahmet Serdar Yilmaz; Ahmet Alkan; Musa Hakan Asyali; Alkan, Ahmet; Asyali, Musa H.; Yilmaz, Ahmet S.
Harmonics are the major power quality problems in industrial and commercial power systems. Several methods for detection of power system harmonics have been investigated by engineers due to increasing harmonic pollution. Since the non-integer multiple harmonics (inter and sub-harmonics) become wide spread the importance of harmonic detection has increased for sensitive filtration. This paper suggests parametric spectral estimation methods for the detection of harmonics inter-harmonics and sub-harmonics. Yule Walker Burg Covariance and Modified Covariance methods are applied to generate cases. Not only integer multiple harmonics but also non-integer multiple harmonics are successfully determined in the computer simulations. Further performances of proposed methods are compared with each other in terms of frequency resolution. © 2007 Elsevier B.V. All rights reserved. © 2008 Elsevier B.V. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 35
Determining a continuous marker for sleep depth
(Pergamon-Elsevier Science Ltd, 2007) Musa Hakan Asyali; Richard Barnett Berry; Michael C.K. Khoo; Ayşe Asyali Altinok; Khoo, Michael C.K.; Asyali, Musa H.; Berry, Richard B.; Altinok, Ayse
Detection and quantification of sleep arousals is an important issue as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG non-rapid eye movement sleep is staged into four somewhat rough categories. In this study we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep. In order to develop such a marker we obtained the time-frequency map of two EEG channels around sleep arousals and identified the frequency bands that show the most change during arousals. We then evaluated classification performance of the potential signals for representing the depth of sleep using receiver operating characteristic analysis. Our comparisons based on the area under the curve values revealed that the sum of absolute powers in alpha and beta bands is a good continuous marker to represent the depth of sleep. Higher values of this marker indicate low-quality sleep and vice versa. We believe that use of this marker will lead to a better quantification of sleep quality. © 2007. © 2008 Elsevier B.V. All rights reserved., MEDLINE® is the source for the MeSH terms of this document.
Citation - WoS: 9
Citation - Scopus: 13
Gene expression profile class prediction using linear Bayesian classifiers
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Musa H. Asyali; Asyali, Musa H.
Due to recent advances in DNA microarray technology using gene expression profiles diagnostic category of tissue samples can be predicted with high accuracy. In this study we discuss shortcomings of some existing gene expression profile classification methods and propose a new approach based on linear Bayesian classifiers. In our approach we first construct gene-level linear classifiers to identify genes that provide high class-prediction accuracies i.e. low error rates. After this screening phase starting with the gene that offers the lowest error rate we construct a multi-dimensional linear classifier by incorporating next best-per-forming genes until the prediction error becomes minimum or 0 if possible. When we compared classification performance of our approach against prediction analysis of microarrays (PAM) and support vector machines (SVM) based approaches we found that our method outperforms PAM and produces comparable results with SVM. In addition we observed that the gene selection scheme of PAM could be misleading. Albeit SVM achieves relatively higher prediction performance it has two major disadvantages: Complexity and lack of insight about important genes. Our intuitive approach offers competing performance and also an efficient means for finding important genes. (c) 2007 Elsevier Ltd. All rights reserved.
Citation - WoS: 107
Gene expression profile classification: A review
(BENTHAM SCIENCE PUBL LTD, 2006) Musa H. Asyali; Dilek Colak; Omer Demirkaya; Mehmet S. Inan; Asyali, Musa H.; Colak, Dilek; Demirkaya, Omer; Inan, Mehmet S.
In this review we have discussed the class-prediction and discovery methods that are applied to gene expression data along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data curse of dimensionality feature extraction/selection and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining) built-in feature selection ability to report prediction strength and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously in detail.