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] De Gennaro, L., Ferrara, M. Sleep spindles: an overview. Sleep Med
Rev; pp. 7:423-40, 2003.
[2] Ktonas, P.Y., Golemati, S., Xanthopoulos, P. , Sakkalis, V., Ortigueira,
M.D, et al. Time-frequency analysis methods to quantify the timevarying
microstructure of sleep EEG spindles: Possibility for dementia
biomarkers? J. of Neuroscience Methods, Vol 185-1: 133-142, 2009.
[3] Causa L., Held C.M., Causa J., Estévez P.A., Perez C.A., Chamorro R.,
Garrido M., Algarín C., Peirano P. 2010. Automated sleep-spindle
detection in healthy children polysomnograms. s.l. : IEEE Trans Biomed
Eng.;57(9):2135-46, 2010.
[4] Steriade, M., Jones, E.G., Llinas, R.: Thalamic Oscillations and
Signaling. Neuroscience Institute Publications. John Wiley & Sons, New
York (1990)
[5] Ahmed B., Redissi A., Tafreshi R. 2009. An automatic sleep spindle
detector based on wavelets and the teager energy operato. s.l. : Annual
International Conference of the IEEE Engineering in Medicine and
Biology Society. IEEE Engineering in Medicine and Biology Society.
Conference 1:2596-9, 2009.
[6] Duman, F., Erogul, O., Telatar, Z., & Yetkin, S. Automatic sleep
spindle detection and localization algorithm. Antalya, Turkey, 2005.
[7] Gör├╝r D., Halici U., Aydin H., Ongun G., Ozgen F., Leblebicioglu K.
2003. , Sleep Spindles Detection Using Autoregressive Modeling. s.l. :
Proc. of ICANN/ICONIP, 2003.
[8] Ventouras E., Monoyiou E., Ktonas P., Paparrigopoulos T., Dikeos D.,
Uzunoglu N., Soldatos C. 2005. Sleep Spindle Detection Using Artificial
Neural Networks Trained with Filtered Time-Domain EEG: A
Feasibility Study. s.l. : Computer Methods and Programs in Biomedicine
78(3):191-207, 2005.
[9] Duman F., Erdamar A., Erogul O., Telatar Z., Yetkin S. 2009. Efficient
sleep spindle detection algorithm with decision tree. s.l. : Expert
Systems with Applications, Vol. 36, No. 6. pp. 9980-9985, 2009.
[10] Causa L., Held C.M., Causa J., Estévez P.A., Perez C.A., Chamorro R.,
Garrido M., Algarín C., Peirano P. 2010. Automated sleep-spindle
detection in healthy children polysomnograms. s.l. : IEEE Trans Biomed
Eng.;57(9):2135-46, 2010.
[11] Proakis, J., Manolakis, D., Digital Signal Processing, 4th Ed., Prentice-
Hall, 2006.
[12] Omerhodzic, I., Avdakovic,S., Nuhanovic, A., Dizdarevic, K. and
Rotim, K. Energy Distribution of EEG Signal Components by Wavelet
Transform, pp45-60 IInTech publishing, 2012
[13] Rechtschaffen, A, Kales, A. A manual of standardised terminology,
techniques and scoring system for sleep stages of human subjects.
Washington, DC: Public Health Service, U.S. Government Printing
Office; 1968.
[14] Costa, J., Ortigueira, M., Batista, A. Short Time Fourier Transform and
Automatic Visual Scoring for the detection of Sleep Spindles. DOCEIS
2012. Springer, IFIP AICT series v.372, p. 267-272.
[15] Devuyst, S., Dutoit, T., Didier, J. F. et al. Automatic sleep spindle
detection in patients with sleep disorders. Conf. Proc. IEEE Eng. Med.
Biol. Soc. 1: 3883-3886, 2006.
[16] Costa, J., Ortigueira, M.D., Batista, A., Paiva, T., "Threshold choice for
automatic spindle detection". Proc. IWSSIP2012; 2012
[17] Schönwald, S., Santa-Helena, E., Rossatto, R., Chaves, M. and Gerhardt,
G. Benchmarking matching pursuit to find sleep spindles, Journal of
Neuroscience Methods Vol 156 1-2: 314-321, 2006.
[1] De Gennaro, L., Ferrara, M. Sleep spindles: an overview. Sleep Med
Rev; pp. 7:423-40, 2003.
[2] Ktonas, P.Y., Golemati, S., Xanthopoulos, P. , Sakkalis, V., Ortigueira,
M.D, et al. Time-frequency analysis methods to quantify the timevarying
microstructure of sleep EEG spindles: Possibility for dementia
biomarkers? J. of Neuroscience Methods, Vol 185-1: 133-142, 2009.
[3] Causa L., Held C.M., Causa J., Estévez P.A., Perez C.A., Chamorro R.,
Garrido M., Algarín C., Peirano P. 2010. Automated sleep-spindle
detection in healthy children polysomnograms. s.l. : IEEE Trans Biomed
Eng.;57(9):2135-46, 2010.
[4] Steriade, M., Jones, E.G., Llinas, R.: Thalamic Oscillations and
Signaling. Neuroscience Institute Publications. John Wiley & Sons, New
York (1990)
[5] Ahmed B., Redissi A., Tafreshi R. 2009. An automatic sleep spindle
detector based on wavelets and the teager energy operato. s.l. : Annual
International Conference of the IEEE Engineering in Medicine and
Biology Society. IEEE Engineering in Medicine and Biology Society.
Conference 1:2596-9, 2009.
[6] Duman, F., Erogul, O., Telatar, Z., & Yetkin, S. Automatic sleep
spindle detection and localization algorithm. Antalya, Turkey, 2005.
[7] Gör├╝r D., Halici U., Aydin H., Ongun G., Ozgen F., Leblebicioglu K.
2003. , Sleep Spindles Detection Using Autoregressive Modeling. s.l. :
Proc. of ICANN/ICONIP, 2003.
[8] Ventouras E., Monoyiou E., Ktonas P., Paparrigopoulos T., Dikeos D.,
Uzunoglu N., Soldatos C. 2005. Sleep Spindle Detection Using Artificial
Neural Networks Trained with Filtered Time-Domain EEG: A
Feasibility Study. s.l. : Computer Methods and Programs in Biomedicine
78(3):191-207, 2005.
[9] Duman F., Erdamar A., Erogul O., Telatar Z., Yetkin S. 2009. Efficient
sleep spindle detection algorithm with decision tree. s.l. : Expert
Systems with Applications, Vol. 36, No. 6. pp. 9980-9985, 2009.
[10] Causa L., Held C.M., Causa J., Estévez P.A., Perez C.A., Chamorro R.,
Garrido M., Algarín C., Peirano P. 2010. Automated sleep-spindle
detection in healthy children polysomnograms. s.l. : IEEE Trans Biomed
Eng.;57(9):2135-46, 2010.
[11] Proakis, J., Manolakis, D., Digital Signal Processing, 4th Ed., Prentice-
Hall, 2006.
[12] Omerhodzic, I., Avdakovic,S., Nuhanovic, A., Dizdarevic, K. and
Rotim, K. Energy Distribution of EEG Signal Components by Wavelet
Transform, pp45-60 IInTech publishing, 2012
[13] Rechtschaffen, A, Kales, A. A manual of standardised terminology,
techniques and scoring system for sleep stages of human subjects.
Washington, DC: Public Health Service, U.S. Government Printing
Office; 1968.
[14] Costa, J., Ortigueira, M., Batista, A. Short Time Fourier Transform and
Automatic Visual Scoring for the detection of Sleep Spindles. DOCEIS
2012. Springer, IFIP AICT series v.372, p. 267-272.
[15] Devuyst, S., Dutoit, T., Didier, J. F. et al. Automatic sleep spindle
detection in patients with sleep disorders. Conf. Proc. IEEE Eng. Med.
Biol. Soc. 1: 3883-3886, 2006.
[16] Costa, J., Ortigueira, M.D., Batista, A., Paiva, T., "Threshold choice for
automatic spindle detection". Proc. IWSSIP2012; 2012
[17] Schönwald, S., Santa-Helena, E., Rossatto, R., Chaves, M. and Gerhardt,
G. Benchmarking matching pursuit to find sleep spindles, Journal of
Neuroscience Methods Vol 156 1-2: 314-321, 2006.
@article{"International Journal of Medical, Medicine and Health Sciences:51303", author = "J. Costa and M. Ortigueira and A. Batista and T. Paiva", title = "An Automatic Sleep Spindle Detector based on WT, STFT and WMSD", abstract = "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.", keywords = "EEG, Short Time Fourier Transform, Sleep Spindles,
Wave Morphology for Spindle Detection, Wavelet Transform.", volume = "6", number = "8", pages = "372-4", }
