An Automatic Sleep Spindle Detector based on WT, STFT and WMSD

Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Sleep Spindles are also promising objective indicators for neurodegenerative disorders. Visual spindle scoring however is a tedious workload. In this paper three different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform, Wavelet Transform and Wave Morphology for Spindle Detection. In order to improve the results, a combination of the three detectors is presented and comparison with human expert scorers is performed. The best performance is obtained with a combination of the three algorithms which resulted in a sensitivity and specificity of 94% when compared to human expert scorers.

[1]  Osman Erogul,et al.  Efficient sleep spindle detection algorithm with decision tree , 2009, Expert Syst. Appl..

[2]  Samir Avdakovic,et al.  Energy Distribution of EEG Signal Components by Wavelet Transform , 2012 .

[3]  M. Ortigueira,et al.  Threshold choice for automatic spindle detection , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[4]  Nikolaos K. Uzunoglu,et al.  Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study , 2005, Comput. Methods Programs Biomed..

[5]  M. Ferrara,et al.  Sleep spindles: an overview. , 2003, Sleep medicine reviews.

[6]  Beena Ahmed,et al.  An automatic sleep spindle detector based on wavelets and the teager energy operator , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Y. Istefanopulos,et al.  IEEE Engineering in Medicine and Biology Society , 2019, IEEE Transactions on Biomedical Engineering.

[8]  Manuel Duarte Ortigueira,et al.  Short Time Fourier Transform and Automatic Visual Scoring for the Detection of Sleep Spindles , 2012, DoCEIS.

[9]  Osman Erogul,et al.  Automatic sleep spindle detection and localization algorithm , 2005, 2005 13th European Signal Processing Conference.

[10]  Claudio A. Perez,et al.  Automated Sleep-Spindle Detection in Healthy Children Polysomnograms , 2010, IEEE Transactions on Biomedical Engineering.

[11]  Günther J. L. Gerhardt,et al.  Benchmarking matching pursuit to find sleep spindles , 2006, Journal of Neuroscience Methods.

[12]  M. Zervakis,et al.  Time–frequency analysis methods to quantify the time-varying microstructure of sleep EEG spindles: Possibility for dementia biomarkers? , 2009, Journal of Neuroscience Methods.

[13]  E. G. Jones,et al.  Thalamic oscillations and signaling , 1990 .

[14]  T. Dutoit,et al.  Automatic Sleep Spindle Detection in Patients with Sleep Disorders , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.