Automated detection of sleep spindles

Görür, Dilan
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 sleep EEG prepared by an expert would be too time consuming and it may be not objective for some spindle regions. Therefore, an automated detection system would assist the expert. In this thesis, a system for automated detection of sleep spindles in the EEG has been developed and tested on the recorded data of normal and insomniac subjects. Different methods for the automated detection of sleep spindles in EEG recordings are investigated. Features from the data are extracted by using two different approaches, short time Fourier transform (STFT) and autoregressive (AR) modeling. Multilayer perceptron (MLP) and also support vector machines (SVM) are utilized as classifiers for comparison. The best classification performances of MLP are found to be 97.5% and 93.6% for STFT and AR model features respectively. The best performances of SVM are found to be 97.5% for STFT and 94.4% for AR model. It is demonstrated that the classifiers trained by a healthy subject's EEG could perform well on another healthy subject's EEG but poorly on an insomniac subject's


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...
Leveraging Multimodal and Feature Selection Approaches to Improve Sleep Apnea Classification Performance
Memiş, Gökhan; Sert, Mustafa; Yazıcı, Adnan (2017-05-15)
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...
An efficient fast method of snore detection for sleep disorder investigation
Çavuşoğlu, Mustafa; Serinağaoğlu Doğrusöz, Yeşim; Department of Electrical and Electronics Engineering (2007)
Snores are breath sounds that most people produce during sleep and they are reported to be a risk factor for various sleep disorders, such as obstructive sleep apnea syndrome (OSAS). Diagnosis of sleep disorders relies on the expertise of the clinician that inspects whole night polysomnography recordings. This inspection is time consuming and uncomfortable for the patient. There are surgical and therapeutic treatments. However, evaluation of the success of these methods also relies on subjective criteria an...
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.
Investigation of sequential properties of snoring episodes for obstructive sleep apnoea identification
ÇAVUŞOĞLU, MUSTAFA; Çiloğlu, Tolga; Serinağaoğlu Doğrusöz, Yeşim; Kamaşak, Mustafa Ersel; EROĞUL, OSMAN; AKÇAM, TİMUR (IOP Publishing, 2008-08-01)
In this paper, 'snore regularity' is studied in terms of the variations of snoring sound episode durations, separations and average powers in simple snorers and in obstructive sleep apnoea (OSA) patients. The goal was to explore the possibility of distinguishing among simple snorers and OSA patients using only sleep sound recordings of individuals and to ultimately eliminate the need for spending a whole night in the clinic for polysomnographic recording. Sequences that contain snoring episode durations (SE...
Citation Formats
D. Görür, “Automated detection of sleep spindles,” M.S. - Master of Science, Middle East Technical University, 2003.