An Efficient Visibility Graph Similarity Algorithm and Its Application on Sleep Stages Classification
This paper presents an efficient horizontal visibility directed graph similarity algorithm (HVDS) by taking the advantages of two synchronization measuring methods in graph theory: phase locking value (PLV) and visibility graph similarity (VGS). It develops a new linear horizontal visibility graph constructing algorithm, analyzes its constructing complexity, and tests its feature performance via the sleep stages identification application. Six features are extracted, separately, from HVDS, PLV and VGS as the input to a support vector machine to classify the seven sleep stages. 11,120 data segments are used for the experiments with each segment lasts 30 seconds. The training sets are selected from a single subject and the testing sets are selected from multiple subjects. 10-cross-validation is employed to evaluate the performances of the PLV, VGS and HVDS methods. The experimental results show that the PLV, VGS and HVDS algorithms produce an average classification accuracy of 72.3%, 81.5% and 82.6%, respectively. The speed of the HVDS is 39 times faster than the VGS algorithm.
KeywordsComputational complexity phase locking value horizontal visibility directed graph similarity classification sleep stage synchronization
Unable to display preview. Download preview PDF.
- 6.He, A., Yang, X., Yang, X., Ning, X.: Phase Synchronization in Sleep Electroencephalogram. In: 2007 IEEE/ICME International Conference on Complex Medical Engineering (CME 2007), pp. 1421–1424 (2007)Google Scholar
- 7.Acharya, A., Kar, S., Routray, A.: Phase synchronization based weighted networks for classifying levels of fatigue and sleepiness. In: 2010 International Conference on Systems in Medicine and Biology (ICSMB), pp. 265–268 (2010)Google Scholar
- 11.Shao, Z.-G.: Network analysis of human heartbeat dynamics. Applied Physics Letters 96, 073703 (2010)Google Scholar
- 15.Elsner, J.B., Jagger, T.H., Fogarty, E.A.: Visibility network of United States hurricanes. Geophys. Res. Lett. 36, L16702 (2009)Google Scholar
- 16.Stam, C.J.: http://home.kpn.nl/stam7883/brainwave.html
- 17.Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 21–27 (2011)Google Scholar
- 18.Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101, e215–e220 (2000)CrossRefGoogle Scholar
- 20.Rechtschaffen, A., Kales, A.: A manual of standardized terminology, techniques and scoring systems for sleep stages of human subjects. In: Office, U.G.P. (ed.) Public Health Service, Washington DC (1968)Google Scholar