A. Varoneckas 1, G. Varoneckas 2,3, A. Zilinskas 4

Transcription

1 A. Varoneckas 1, G. Varoneckas 2,3, A. Zilinskas 4 a.varoneckas@if.vdu.lt giedvar@ktl.mii.lt antanasz@ktl.mii.lt 1 Faculty of Applied Informatics, Vytautas Magnus University, Vileikos Str. 8, Kaunas, Lithuania 2 Institute of Psychophysiology and Rehabilitation, Vyduno Str. 4, Palanga, Lithuania 3 Mechatronics Science Institute, Klaipeda University, Herkaus Manto Str. 84, Klaipeda, Lithuania, 4 Institute of Mathematics and Informatics, Akademijos Str. 4, Vilnius, Lithuania

2 Agenda Introduction Heart rate patterns (RR time series characteristics) / Problem formulation Multiclass classification methods GRID infrastructure Grid implementation Results Discussion Conclusions

3 Heart Rate and HR Variability during Sleep Zemaityte D., Varoneckas G., Sokolov E. Psychophysiology, 1984, 21(3),

4 Heart Rate Sleep Pattern during Sleep Zemaityte D., Varoneckas G. Sokolov E. Psychophysiology, 1984, 21(3),

5 Electrocardiogram Sequences of time of the R waves Sequence may be analyzed as a sequence of counts {N j }(T) in a predetermined time interval T Or as a sequence of interbeat (RR) intervals Malvin C. Teich, S. B. Lowen, B. M. Jost, K. Vibe-Rheymer, and C. Heneghan, Heart Rate Variability: Measures and Models. In Nonlinear Biomedical Signal Processing Vol. II: Dynamic Analysis and Modeling, edited by M. Akay.

6 Hypnogram Rhythmogram (RR interval sequence)

7 Linear Heart Rate Parameters Mean, Standard deviation Very low frequency component (VLF) Low frequency component (LF) High frequency component (HF) LF/HF ratio Nonlinear Heart Rate Parameters Approximate entropy (ApEn) measures the complexity or irregularity of the signal Detrended fluctuation analysis Short-term fluctuations Long-term fluctuations Progressive detrended fluctuation analysis (Slope)

8 Task Identification of sleep stages based on extracted features from RR interval time series is a multiclass classification problem

9 Multiclass Classification One versus all This approach assumes that t for each class there exist it a standard binary classifier which separates that class and all other classes. One versus one The number of (k(k 1))/2 binary classifiers were constructed. An example belongs to the class which assigns to it the highest value. Directed Acyclic Graph (DAG)

10 Full DAG for Multiclass l Classification into 6 Sleep Stages not 1 1 vs 6 not vs 6 1 vs not 2 not 6 not 1 not vs 6 2 vs vs not 3 not 6 not 2 not 5 not 1 not vs 6 3 vs vs vs not 4 not 6 not 3 not 5 not 2 not 4 not 1 not 3 5vs6 4vs5 3 vs 4 2 vs 3 1 vs

11 DAG of Grouped Vertices 1,2,3,4 vs 5,6 1,2 vs 3,4 5 vs 6 1 vs 2 3 vs

12 GRID Computing Grid computing (the use of a computational grid) is applying the resources of many computers in a network to a single problem at the same time usually to a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data

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15 LitGrid Clusters Cluster #CPU CPU Type Memory Storage MII LCG2 68 Intel CORE2QUAD Q MHz 2048 KTU ELEN LCG2 12 Intel P4 3.0 MHz TB SU GRID VDU IF LCG2 12 Intel P4 3.0 MHz 1024 VU MIF LCG2 26 AMD Opteron 2.4 MHz TB ITPA LCG2 VGTU glite 53 Intel Pentium(R) Dual Core 2.6 MHz TB FI LCG2 13 AMD Opteron 2.4 MHz 2048 KMU PRI LCG2 12 Intel P4 3.0 MHz TB KU CORPI 18 Intel XEON 1.6 MHz TB LEI GLITE 10 AMD Phenom MHz TB KTU BG GLITE PANKO GRID 6 Intel PIII 2.0 MHz 256 MARKO GRID 5 Intel Core2 2.8 MHz 2048 AKOLEGIJA GRID 5 Intel Core2 2.4 MHz 4096 NA NA NA

16 LitGRID Infrastructure

17 Networking LitGrid is based on LitNet infrastructure. LitNet Lithuanian Academic and Research Network

18 GRID Middleware and Software EGEE based middleware on Scientific Linux clusters glite v.3.0 LCG v.2.7 MPI implementation (MPICH) VO Litgrid, Gamess, Balticgrid Software ATLAS (Automatically Tuned Linear Algebra Software) GAMESS (General Atomic and Molecular Electronic Structure System) GNU C/C++, fortran compilers

19 Grid Implementation DAG jobs a set of jobs where ethe input, output, or execution of one or more jobs depends on one or more other jobs Full DAG and DAG of grouped vertices

20 Data Sets Data # training #testing # attributes # classes set data data St1 Set Set

21 Classification Methods Support vector machines (SVM) SVM light * Kernels used: Linear Polynomial RBF T. Joachims, Making Large-Scale SVM Learning Practical, in Advances in Kernel Methods Support Vector Learning. B. Scholkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999

22 Classification Problem Full DAG DAG of grouped vertices Training time #SVs Rate Training time #SVs Rate Linear kernel function Set % % St2 Set % % Polynomial kernel function Set % % 63% Set % % RBF kernel function Set % % Set % %

23 Conclusions Gridified DAG multiclass classification can be used for identification of sleep states using RR interval data efficiently Polynomial kernel function has better classification rate; however training time was less using RBF kernel function

24 Thank You!!!