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Sleep spindles detection using short time Fourier transform and neural networks
Date
2002-01-01
Author
Gorur, Dilan
Halıcı, Uğur
Aydin, Hamdullah
Ongun, Guclu
Ozgen, Fuat
Leblebicioglu, Kemal
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Sleep spindles are a hallmark of the stage 2 sleep. Their distribution over the non-REM sleep is clinically important. In this paper, a method that detects the sleep spindles in sleep EEG is proposed. Short time Fourier Transform is used for feature extraction. Both multilayer perceptron and Support Vector Machine are utilized in detection of the spindles in sleep EEG for comparison. The classification performance of MLP is found to be 88.7% and that of SVM as 95.4%. It should be noted that there might be differences also in visual scoring by experts, so the results obtained are quite satisfactory.
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0036082854&origin=inward
https://hdl.handle.net/11511/87697
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Sleep spindles detecton using short time fourier transform and neural networks
Gorur, Dilan; Halıcı, Uğur; Aydın, Hamdullah; Ongun, Güçlü; Özgen, Fuat; Leblebicioğlu, Mehmet Kemal (2002-05-17)
Sleep spindles are a hallmark of the stage 2 sleep. Their distribution over the non-REM sleep is clinically important. In this paper, a method that detects the sleep spindles in sleep EEG is proposed. Short time Fourier transform is used for feature extraction. Both multilayer perceptron and Support Vector Machine are utilized in detection of the spindles in sleep EEG for comparison. The classification performance of MLP is found to be 88.7% and that of SVM as 95.4%. It should be noted that there might be d...
Sleep spindles detection using autoregressive modeling
GORUR, DILAN; Halıcı, Uğur; AYDIN, HAMDULLAH; GUCLU, ONGUN; ÖZGEN, FUAT; Leblebicioğlu, Mehmet Kemal (2003-06-29)
Being one of the well-defined and functional rhythmic activities observed in sleep EEG, sleep spindles are significant for brain research. Visual detection of sleep spindles is very time consuming and subjective. In this study, automated spindle detection by using AR modeling for feature extraction is proposed. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used as classifiers for comparison. Performances were found as 93.6% for the MLP and 94.4% for the SVM classifiers.
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...
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Leveraging Multimodal and Feature Selection Approaches to Improve Sleep Apnea Classification Performance
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Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust fea...
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D. Gorur, U. Halıcı, H. Aydin, G. Ongun, F. Ozgen, and K. Leblebicioglu, “Sleep spindles detection using short time Fourier transform and neural networks,” 2002, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0036082854&origin=inward.