Bispectral analysis of single channel EEG to estimate macro-sleep-architecture

Estimation of macro-sleep-architecture (MSA) is a critical process in assessing several sleep disorders such as obstructive sleep apnoea, periodic leg movement disorder, upper-airway resistance syndrome, etc. MSA is defined as classification of sleep into three major states: state wake, state REM and state NREM. Existing methods of MSA analysis use six channels of electrophysiological signals (EEG, EOG and EMG). They depend on the manual scoring of overnight data records using the R&K criteria (1968), developed for visual analysis of signals based on morphological features. Manual scoring is cumbersome, subjective and not suitable for portable devices used for community screening of sleep disorders. To address this issue, we propose a fully automated technology for MSA estimation based on a single channel of EEG data. The proposed technology was compared, on a clinical database of 47 patients, with that of an expert human scorer. The average agreement between the human and the proposed technology was found to be 76 ± 7.5% (kappa = 0.51 ± 0.14). The proposed method estimates MSA using simplified instrumentation making it possible to extend EEG/MSA to portable systems as well; method uses low-computation-load bispectrum techniques independent of R&K criteria (1968) making real-time automated analysis a reality. Copyrigh

Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.The authors would like to thank Universidad Autonoma de Manizales for financial support in the present work (Research project 328-038). This work has also been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222.Rodríguez-Sotelo, JL.; Osorio-Forero, A.; Jiménez-Rodríguez, A.; Cuesta Frau, D.; Cirugeda Roldán, EM.; Peluffo, D. (2014). Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques. Entropy. 16(12):6573-6589. https://doi.org/10.3390/e16126573S657365891612Saper, C. B., Fuller, P. M., Pedersen, N. P., Lu, J., & Scammell, T. E. (2010). Sleep State Switching. Neuron, 68(6), 1023-1042. doi:10.1016/j.neuron.2010.11.032RAUCHS, G., DESGRANGES, B., FORET, J., & EUSTACHE, F. (2005). The relationships between memory systems and sleep stages. Journal of Sleep Research, 14(2), 123-140. doi:10.1111/j.1365-2869.2005.00450.xLandmann, N., Kuhn, M., Piosczyk, H., Feige, B., Baglioni, C., Spiegelhalder, K., … Nissen, C. (2014). The reorganisation of memory during sleep. Sleep Medicine Reviews, 18(6), 531-541. doi:10.1016/j.smrv.2014.03.005Hublin, C., Partinen, M., Koskenvuo, M., & Kaprio, J. (2007). Sleep and Mortality: A Population-Based 22-Year Follow-Up Study. Sleep, 30(10), 1245-1253. doi:10.1093/sleep/30.10.1245The role of polysomnography in the differential diagnosis of chronic insomnia. (1988). American Journal of Psychiatry, 145(3), 346-349. doi:10.1176/ajp.145.3.346Steriade, M., McCormick, D., & Sejnowski, T. (1993). Thalamocortical oscillations in the sleeping and aroused brain. Science, 262(5134), 679-685. doi:10.1126/science.8235588DANKER-HOPFE, H., ANDERER, P., ZEITLHOFER, J., BOECK, M., DORN, H., GRUBER, G., … DORFFNER, G. (2009). Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. Journal of Sleep Research, 18(1), 74-84. doi:10.1111/j.1365-2869.2008.00700.xDanker-Hopfe, H., Kunz, D., Gruber, G., Klösch, G., Lorenzo, J. L., Himanen, S. L., … Dorffner, G. (2004). Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders. Journal of Sleep Research, 13(1), 63-69. doi:10.1046/j.1365-2869.2003.00375.xVuckovic, A., Radivojevic, V., Chen, A. C. N., & Popovic, D. (2002). Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering & Physics, 24(5), 349-360. doi:10.1016/s1350-4533(02)00030-9Robert, C., Guilpin, C., & Limoge, A. (1998). Review of neural network applications in sleep research. Journal of Neuroscience Methods, 79(2), 187-193. doi:10.1016/s0165-0270(97)00178-7Ronzhina, M., Janoušek, O., Kolářová, J., Nováková, M., Honzík, P., & Provazník, I. (2012). Sleep scoring using artificial neural networks. Sleep Medicine Reviews, 16(3), 251-263. doi:10.1016/j.smrv.2011.06.003Subasi, A., & Erçelebi, E. (2005). Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2), 87-99. doi:10.1016/j.cmpb.2004.10.009Rodríguez-Sotelo, J. L., Peluffo-Ordoñez, D., Cuesta-Frau, D., & Castellanos-Domínguez, G. (2012). Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering. Computer Methods and Programs in Biomedicine, 108(1), 250-261. doi:10.1016/j.cmpb.2012.04.007Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberye, J. J. L. (2000). Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering, 47(9), 1185-1194. doi:10.1109/10.867928Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Van Sweden, B., Kemp, B., Kamphuisen, H. A. C., & Van der Velde, E. A. (1990). Alternative Electrode Placement in (Automatic) Sleep Scoring (F pz-Cz/P z-Oz versus C4-At ). Sleep, 13(3), 279-283. doi:10.1093/sleep/13.3.279Mourtazaev, M. S., Kemp, B., Zwinderman, A. H., & Kamphuisen, H. A. C. (1995). Age and Gender Affect Different Characteristics of Slow Waves in the Sleep EEG. Sleep, 18(7), 557-564. doi:10.1093/sleep/18.7.557Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., & Dickhaus, H. (2012). Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier. Computer Methods and Programs in Biomedicine, 108(1), 10-19. doi:10.1016/j.cmpb.2011.11.005Shoupeng, S., & Peiwen, Q. (2007). A fractal-dimension-based signal-processing technique and its use for nondestructive testing. Russian Journal of Nondestructive Testing, 43(4), 270-280. doi:10.1134/s1061830907040080Peng, C. ‐K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82-87. doi:10.1063/1.166141Fell, J., Röschke, J., Mann, K., & Schäffner, C. (1996). Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalography and Clinical Neurophysiology, 98(5), 401-410. doi:10.1016/0013-4694(96)95636-9Pincus, S. (1995). Approximate entropy (ApEn) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 110-117. doi:10.1063/1.166092Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Costa, M., Goldberger, A. L., & Peng, C.-K. (2005). Multiscale entropy analysis of biological signals. Physical Review E, 71(2). doi:10.1103/physreve.71.021906Hansen, P., & Mladenović, N. (2001). J-Means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34(2), 405-413. doi:10.1016/s0031-3203(99)00216-2Güneş, S., Polat, K., & Yosunkaya, Ş. (2010). Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Systems with Applications, 37(12), 7922-7928. doi:10.1016/j.eswa.2010.04.043Koley, B., & Dey, D. (2012). An ensemble system for automatic sleep stage classification using single channel EEG signal. Computers in Biology and Medicine, 42(12), 1186-1195. doi:10.1016/j.compbiomed.2012.09.012Sleep stage classification using Wavelet Transform and neural networkhttp://www.researchgate.net/publication/216570220_Sleep_Stage_Classification_Using_Wavelet_Transform__Neural_NetworkKrakovská, A., & Mezeiová, K. (2011). Automatic sleep scoring: A search for an optimal combination of measures. Artificial Intelligence in Medicine, 53(1), 25-33. doi:10.1016/j.artmed.2011.06.004SHAMBROOM, J. R., FÁBREGAS, S. E., & JOHNSTONE, J. (2011). Validation of an automated wireless system to monitor sleep in healthy adults. Journal of Sleep Research, 21(2), 221-230. doi:10.1111/j.1365-2869.2011.00944.xSwarnkar, V., & Abeyratne, U. R. (2014). Bispectral analysis of single channel EEG to estimate macro-sleep-architecture. International Journal of Medical Engineering and Informatics, 6(1), 43. doi:10.1504/ijmei.2014.058531Liang, S.-F., Kuo, C.-E., Hu, Y.-H., Pan, Y.-H., & Wang, Y.-H. (2012). Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models. IEEE Transactions on Instrumentation and Measurement, 61(6), 1649-1657. doi:10.1109/tim.2012.2187242Weiss, B., Clemens, Z., Bódizs, R., & Halász, P. (2011). Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages. Brain Research Bulletin, 84(6), 359-375. doi:10.1016/j.brainresbull.2010.12.005Šušmáková, K., & Krakovská, A. (2008). Discrimination ability of individual measures used in sleep stages classification. Artificial Intelligence in Medicine, 44(3), 261-277. doi:10.1016/j.artmed.2008.07.005Olbrich, E., Achermann, P., & Wennekers, T. (2011). The sleeping brain as a complex system. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1952), 3697-3707. doi:10.1098/rsta.2011.0199Buckelmüller, J., Landolt, H.-P., Stassen, H. H., & Achermann, P. (2006). Trait-like individual differences in the human sleep electroencephalogram. Neuroscience, 138(1), 351-356. doi:10.1016/j.neuroscience.2005.11.005Van Dongen, H. P. A., Vitellaro, K. M., & Dinges, D. F. (2005). Individual Differences in Adult Human Sleep and Wakefulness: Leitmotif for a Research Agenda. Sleep, 28(4), 479-498. doi:10.1093/sleep/28.4.479A digital telemetry system for ambulatory sleep recordinghttp://physionet.mit.edu/pn4/sleep-edfx/Papers/1993-Kemp—telemetry.pdfDijk, D. J., Beersma, D. G. M., Daan, S., & van den Hoofdakker, R. H. (1989). Effects of seganserin, a 5-HT2 antagonist, and temazepam on human sleepsstages and EEG power spectra. European Journal of Pharmacology, 171(2-3), 207-218. doi:10.1016/0014-2999(89)90109-xZhang, Z., Chen, Z., Zhou, Y., Du, S., Zhang, Y., Mei, T., & Tian, X. (2014). Construction of rules for seizure prediction based on approximate entropy. Clinical Neurophysiology, 125(10), 1959-1966. doi:10.1016/j.clinph.2014.02.017Khan, J., Mariappan, R., & Venkatraghavan, L. (2014). Entropy as an indicator of cerebral perfusion in patients with increased intracranial pressure. Journal of Anaesthesiology Clinical Pharmacology, 30(3), 409. doi:10.4103/0970-9185.13728