Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features

Biomedical Signal Processing and Control - Tập 48 - Trang 80-92 - 2019
Wessam Al-Salman1,2, Yan Li1,3, Peng Wen1
1School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia
2Thi-Qar University, College of Education for Pure Science, Iraq
3School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China

Tài liệu tham khảo

Ahmed, 2009, An automatic sleep spindle detector based on wavelets and the teager energy operator, 2596 Akin, 1998, Detection of sleep spindles by discrete wavelet transform, 15 Al Ghayab, 2016, Classification of epileptic EEG signals based on simple random sampling and sequential feature selection, Brain Inf., 3, 85, 10.1007/s40708-016-0039-1 Al-salman, 2018, An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image, Biomed. Signal Process. Control, 41, 210, 10.1016/j.bspc.2017.11.019 Alyasseri, 2017, Electroencephalogram signals denoising using various mother wavelet functions: a comparative analysis, 100 Alyasseri, 2017, Optimal electroencephalogram signals denoising using hybrid β-Hill climbing algorithm and wavelet transform, 106 Andrillon, 2011, Sleep spindles in humans: insights from intracranial EEG and unit recordings, J. Neurosci., 31, 17821, 10.1523/JNEUROSCI.2604-11.2011 Bajaj, 2016, A hybrid method based on time–frequency images for classification of alcohol and control EEG signals, Neural Comput. Appl., 1 Bajaj, 2013, Automatic classification of sleep stages based on the time-frequency image of EEG signals, Comput. Methods Programs Biomed., 112, 320, 10.1016/j.cmpb.2013.07.006 Cleophas, 2016, Non-parametric tests for Three or more samples (friedman and kruskal-Wallis), 193 Cvetkovic, 2008, Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study, Digital Signal Process., 18, 861, 10.1016/j.dsp.2007.05.009 da Costa, 2013, K-means clustering for sleep spindles classification, Int. J. Inf. Technol. Comput. Sci. (IJITCS), 10, 77 Daubechies, 1990, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inf. Theory, 36, 961, 10.1109/18.57199 Devuyst, 2006, Automatic sleep spindle detection in patients with sleep disorders, 3883 Devuyst, 2011, Automatic sleep spindles detection—overview and development of a standard proposal assessment method, 1713 Diykh, 2016, Complex networks approach for EEG signal sleep stages classification, Expert Syst. Appl., 63, 241, 10.1016/j.eswa.2016.07.004 Diykh, 2016 Duman, 2005, Automatic sleep spindle detection and localization algorithm, 1 Farid, 2014, Hybrid decision tree and naïve bayes classifiers for multi-class classification tasks, Expert Syst. Appl., 41, 1937, 10.1016/j.eswa.2013.08.089 Ghaffari, 2010, Segmentation of holter ECG waves via analysis of a discrete wavelet-derived multiple skewness–kurtosis based metric, Ann. Biomed. Eng., 38, 1497, 10.1007/s10439-010-9919-3 Gorur, 2002, Sleep spindles detection using short time fourier transform and neural networks, 1631 Güneş, 2011, Sleep spindles recognition system based on time and frequency domain features, Expert Syst. Appl., 38, 2455, 10.1016/j.eswa.2010.08.034 Huupponen, 2007, Development and comparison of four sleep spindle detection methods, Artif. Intell. Med., 40, 157, 10.1016/j.artmed.2007.04.003 Imtiaz, 2013, Automatic detection of sleep spindles using teager energy and spectral edge frequency, 262 Jaleel, 2014, Improved spindle detection through intuitive pre-processing of electroencephalogram, J. Neurosci. Methods, 233, 1, 10.1016/j.jneumeth.2014.05.009 Krohne, 2014, Detection of K-complexes based on the wavelet transform, 5450 l Samborski, 2011, Hybrid wavelet-fourier-HMM speaker recognition, Int. J. Hybrid Inf. Technol., 4, 25 Lee, 1999, Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current, Int. J. Adv. Manuf. Technol., 15, 238, 10.1007/s001700050062 Li, 2011, Clustering technique-based least square support vector machine for EEG signal classification, Comput. Methods Programs Biomed., 104, 358, 10.1016/j.cmpb.2010.11.014 Nonclercq, 2013, Sleep spindle detection through amplitude–frequency normal modelling, J. Neurosci. Methods, 214, 192, 10.1016/j.jneumeth.2013.01.015 Ocak, 2009, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Syst. Appl., 36, 2027, 10.1016/j.eswa.2007.12.065 O’Reilly, 2015, Combining time-frequency and spatial information for the detection of sleep spindles, Front. Hum. Neurosci., 9, 70 Orhan, 2011, EEG signals classification using the K-means clustering and a multilayer perceptron neural network model, Expert Syst. Appl., 38, 13475, 10.1016/j.eswa.2011.04.149 Parekh, 2015, Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization, J. Neurosci. Methods, 251, 37, 10.1016/j.jneumeth.2015.04.006 Patti, 2014, Automated sleep spindle detection using novel EEG features and mixture models, 2221 Quinlan, 1986, Induction of decision trees, Mach. Learn., 1, 81, 10.1007/BF00116251 Rechtschaffen, 1968 Şen, 2014, A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms, J. Med. Syst., 38, 1, 10.1007/s10916-014-0018-0 Smitha, 2015, Analysis of fractal dimension of EEG signals under mobile phone radiation, 1 Tarasiuk, 2004, Hybrid wavelet-fourier spectrum analysis, IEEE Trans. Power Delivery, 19, 957, 10.1109/TPWRD.2004.824398 Tsanas, 2015, Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing, Front. Hum. Neurosci., 9, 181, 10.3389/fnhum.2015.00181 Wang, 2003, Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition, Pattern Recognit., 36, 2429, 10.1016/S0031-3203(03)00044-X Warby, 2014, Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods, Nat. Methods, 11, 385, 10.1038/nmeth.2855 Wilson, 2000, Reduction techniques for instance-based learning algorithms, Mach. Learn., 38, 257, 10.1023/A:1007626913721 Yücelbas, 2016, Detection of sleep spindles in sleep EEG by using the PSD methods, Indian J. Sci. Technol., 9, 10.17485/ijst/2016/v9i25/96628 Yücelbaş, 2016, Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods, Neural Comput. Appl., 1 Zheng, 2003, Novel feature extraction method-wavelet-fourier analysis and its application to glaucoma classification, 672 Zhuang, 2016, Enhanced automatic sleep spindle detection: a sliding window-based wavelet analysis and comparison using a proposal assessment method, 11 Ziółko, 2011, Hybrid wavelet-fourier-HMM speaker recognition, Int. J. Hybrid Inf. Technol., 4, 25