Detection of Arousal in Patients with Respiratory Sleep Disorder Using Single Channel EEG

단일 채널 뇌전도를 이용한 호흡성 수면 장애 환자의 각성 검출

  • 조성필 (연세대학교 의료공학협동과정) ;
  • 최호선 (대원과학대 의료정보시스템과) ;
  • 이경중 (연세대학교 의공학과)
  • Published : 2006.05.01

Abstract

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

Keywords

References

  1. ASDA Report, 'EEG Arousals: Scoring Rules and Examples', Sleep, vol. 15, no. 2, pp. 173-184, 1992
  2. Richard B. Berry and Kevin Gleeson, 'Respiratory Arousal From Sleep: Mechanisms and Significance', Sleep, vol. 20, no. 8, pp. 654-675, 1997
  3. Fabrizio De Carli, Lino Nobili, Paola Gelcich, Franco Ferrillo, 'A Method for the Automatic Detection of Arousals During Sleep', sleep, vol. 22, no. 5, pp. 561-572, 1999
  4. Rajeev Agarwal, 'Automatic Detection of Micro Arousals', Proceedings of the IEEE EMBC'05, Shanghai, China, 2005 https://doi.org/10.1109/IEMBS.2005.1616628
  5. M.J. Drinnan A. Murray, J.E.S. White, A.J. Smithson, G.J. Gibson and C.J. Griffiths, 'Evaluation of activity-based techniques to identify transient arousal in respiratory sleep disorder', J. Sleep Res., 5 : 173-180, 1996 https://doi.org/10.1046/j.1365-2869.1996.d01-71.x
  6. M. Jobert, H. Schulz, P. Jahnig, C. Tismer, F. Bes, and H. Escola, 'A computerized method for detecting episodes of wakefulness during sleep based on the alpha slow wave index (ASI)', Sleep, vol. 17, no. 1, pp. 37-46, 1994
  7. M.J. Drinnan, A. Murray, J.E.S. White, A.J. Smithson, C.J. Griffiths and G.J. Gibson, 'Automated Recognition of EEG Changes Accompanying Arousal in Respiratory Sleep Disorders', Sleep, vol. 19, no. 4, pp. 296-303, 1996
  8. 이광수, 김대식, 최장욱, 뇌파검사학, 고려의학, 2001
  9. Mark. Van Gils, Annelise. Rosenfalck, Steven. White, Pamela. Prior, John. Gade, Lotifi. Senhadji, Carsten. Thomsen, I. Robert. Ghosh, Richard.M. Longford, and Kjeld. Jensen, 'Signal processing in prolonged EEG recordings during intensive care', IEEE Eng. Med. Biol. Mag., vol. 16, no. 6, pp. 56-63, 1997 https://doi.org/10.1109/51.637118
  10. Shie Qian, and Dapang Chen, Joint Time-Frequency Analysis - Methods and Applications, Prentice-Hall, 1996
  11. Fabrizio De Carli, Lino. Nobili, Manolo. Beelke, Tsuyoshi. Watanabe, Arianna. Smerieri, Liborio. Parrino, Mario.Giovanni. Terzano, and Franco. Ferrillo, 'Quantitative analysis of sleep EEG microstructure in the time-frequency domain', Brain Research Bulletin, 63, pp. 399-405, 2004 https://doi.org/10.1016/j.brainresbull.2003.12.013
  12. Bastiaan (Bob) Kemp, Aeilko (Koos) H. Zwinderman, Bert Tuk, Hilbert A. C. Kamphuisen, and Josefien J. L. (Janine) Oberye, 'Analysis of a Sleep-Dependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG', IEEE Trans. Biomed. Eng., vol. 47, no. 9, pp. 1185-1194, 2000 https://doi.org/10.1109/10.867928
  13. Rajeev Agarwal, and Jean Gotman, 'Computer-Assisted Sleep Staging', IEEE Trans. Biomed. Eng., vol. 48, no. 12, pp.1412-1423, 2001 https://doi.org/10.1109/10.966600
  14. 송미혜, 'LDA와 SVM 기반의 심실세동 검출에 관한 연구', 연세대학교 석사학위논문, 2005
  15. 어상준, 'Support Vector Machines를 이용한 문서 정보 기반의 단백질 기능 분류', 서울대학교 석사학위논문, 2003
  16. Scholkopf, B., C. J. C. Burges, and A. J. Smola, Advances in kernal methods, The MIT Press, 1999