Total views : 372
Detection of Sleep Spindles in Sleep EEG by using the PSD Methods
Background/Objectives: In this study, Fast Fourier Transform (FFT), Welch, Autoregressive (AR) and MUSIC methods were implemented to detect sleep spindles (SSs) in Electroencephalogram (EEG) signals by extracting features in frequency space. Methods/Statistical Analysis: A database from these signals of five subjects which were recorded at sleep laboratory of Necmettin Erbakan University in Turkey was ready for use. The database consisted of 600 EEG epochs in total. The number of epochs was 300 for both with and without SSs in this database. Comparison of the performances of these methods on SS determination process was performed by using Artificial Neural Networks (ANN) classifier. Findings: According to the test classification results, notable difference was obtained between the applied PSD methods. By using the extracted all features, maximum test classification accuracies were achieved as 84.83%, 80.67%, 80.83% and 80.33% with use of FFT, Welch, AR and MUSIC, respectively. To determine the SSs, Principal Component Analysis (PCA) also was utilized in this study. When PCA was applied, the results were 89.50%, 82.00%, 93.00% and 94.83% by use of the same PSD methods, respectively. Application/Improvements: As a result, the performance of PCA and MUSIC is better than the others. Hence, these methods can be used safely for automatic detection of SSs.
AR, EEG, FFT, MUSIC, Sleep Spindle, Welch.
- Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. US Government Printing Office: Washington; 1969.
- AASM. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine: Task Force; 1999.
- Jankel WR, Niedermeyer E. Sleep spindles. Journal of Clinical Neurophysiology. 1985; 2:1–35.
- Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, public health service. Washington, DC: U.S. Government Printing Office; 1968.
- Wei HG, Riel E, Czeisler CA, Dijk DJ. Attenuated amplitude of circadian and sleep-dependent modulation of electroencephalographic sleep spindle characteristics in elderly human subjects. Neuroscience Letters. 1999; 260:29–32.
- Nonclercq A, Urbain C, Verheulpen D, Decaestecker C, Bogaert PV, Peigneux P. Sleep spindle detection through amplitude-frequency normal modelling. Journal of Neuroscience Methods. 2013; 214(2):192–203.
- Güneş S, Dursun M, Polat K, Yosunkaya Ş. Sleep spindles recognition system based on time and frequency domain features. Expert Systems with Applications. 2011; 38(3):2455–61.
- Ventouras EM, Monoyiou EA, Ktonas PY, Paparrigopoulos T, Dikeos DG, Uzunoglu NK, Soldatos CR. Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study. Computer Methods and Programs in Biomedicine. 2005; 78(3):191–207.
- Ventouras EM, Panagi M, Tsekou H, Paparrigopoulos TJ, Ktonas Y. Amplitude normalization applied to an artificial neural network- based automatic sleep spindle detection system. Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago: IL; 2014. p. 3240–43.
- Liang S, Kuo C, Hu Y, Chen C, Li Y. An adaptive neuro-fuzzy inference system for sleep spindle detection. Proceedings of International Conference on Fuzzy Theory and Its Applications (iFUZZY 2012), Taichung; 2012. p. 369–73.
- Imtiaz SA, Rodriguez-Villegas E. Evaluating the use of line length for automatic sleep spindle detection. Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago: IL, 2014. p. 5024–27.
- Patti CR, Chaparro-Vargas R, Cvetkovic D. Automated sleep spindle detection using novel EEG features and mixture models. Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago: IL; 2014. p. 2221–44.
- Parekh A, Selesnick IW, Rapoport DM, Ayappa I. Sleep spindle detection using time-frequency sparsity. Proceedings of Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia: PA; 2014. p. 1–6.
- Gorur D, Halici U, Aydin H, Ongun G, Ozgen F, Leblebicioglu K. Sleep spindles detection using short time Fourier transform and neural networks. Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN’02) , Honolulu: HI; 2002. p. 1631–6.
- Venkatakrishnan P, Sangeetha S, Sukanesh R. Detection of sleep spindles from Electroencephalogram (EEG) Signals Using Auto Recursive (AR) Model. Proceedings of First International Conference on Emerging Trends in Engineering and Technology, Nagpur-Maharashtra; 2008. p. 645–8.
- Ahmed B, Redissi A, Tafreshi R. An automatic sleep spindle detector based on wavelets and the teager energy operator. Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis: MN; 2009. p. 2596–99.
- Jiapu P, Willis JT. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985; 32(3):230–6.
- A tutorial on principal components analysis [Internet]. [Cited 2015 Apr 01]. Available from: http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf.
- Wang X, Paliwal KK. Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognition. 2003 Oct; 36(10):2429–39.
- Sinha RK. Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. Journal of Medical Systems. 2008; 32(4):291–9.
- Ebrahimi F, Mikaeili M, Estrada E, Nazeran H. Automatic sleep stage classification based on eeg signals using neural networks and wavelet packet coefficients. Proceedings of 30th Annual Intermational IEEE EMBS Conference, Canada; 2008. p. 1151–4.
- Fast Fourier transform [Internet]. [Cited 2015 Apr 01]. Available from: http://en.wikipedia.org/wiki/Fast_Fourier_transform.
- Welch’s method. http://en.wikipedia.org/wiki/Welch’s_method. Date accessed: 01/04/2015.
- Autoregressive model [Internet]. [Cited 2015 Apr 01]. Available from: http://en.wikipedia.org/wiki/Autoregressive_model.
- Schmidt RO. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation. 1986; 34(3);276–80.
- Junbo L, Daifeng Z, Haibin W. Spatial time-frequency MUSIC algorithm in D stable distribution noise environment. Proceedings of International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), Shenyang: China; 2013. p. 3504–8.
- Peng D, Xiang Y. Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions. Digital Signal Processing, 2010; 20:581–96.
- Lewis D, Gale W. A sequential algorithm for training text classifiers. Annual ACM Conference on Research and Development in Information Retrieval. Proceedings of 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York; 1994. p. 3–12.
- Ma Y, Guo L, Cukic B. A statistical framework for the prediction of fault proneness. Advances in machine learning application in software engineering, Idea Group Inc.; 2006.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.