Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings

Computer Methods and Programs in Biomedicine

Volumn 112, Issue 1, 2013, Pages 189-199

  Martínez Zarzuela, Mario ^a   Gómez, Carlos ^a   Díaz Pernas, Francisco Javier ^a   Fernández, Alberto ^b   Hornero, Roberto ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Cross Approximate Entropy; CUDA; GPGPU; Magnetoencephalography; Neural signal analysis

Indexed keywords

APPROXIMATE ENTROPY; BIOMEDICAL SIGNAL; BIOMEDICAL SIGNAL ANALYSIS; COMPUTATIONAL COSTS; CUDA; GPGPU; MULTIDIMENSIONAL DATA; PARALLEL COMPUTATION;

BIOELECTRIC PHENOMENA; BRAIN MAPPING; MAGNETOENCEPHALOGRAPHY; PROGRAM PROCESSORS; SIGNAL ANALYSIS;

ENTROPY;

ALGORITHM; ARTICLE; BIOMEDICINE; COMPARATIVE STUDY; COMPUTER; CROSS APPROXIMATE ENTROPY; DATA PROCESSING; ENTROPY; MAGNETOENCEPHALOGRAPHY; RECORDING; SIGNAL PROCESSING;

CROSS APPROXIMATE ENTROPY; CUDA; GPGPU; MAGNETOENCEPHALOGRAPHY; NEURAL SIGNAL ANALYSIS;

ADULT; AGED; ALGORITHMS; CHILD; COMPUTER GRAPHICS; FEMALE; HUMANS; MAGNETOENCEPHALOGRAPHY; MALE; MIDDLE AGED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84883757290     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2013.07.005     Document Type: Article

References (56)

1. Pincus S.M. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America 1991, 88:2297-2301.
2. Pincus S.M., Goldberger A.L. Physiological time-series analysis: what does regularity quantify?. American Journal of Physiology 1994, 266:H1643-H1656.
3. Pincus S.M. Assessing serial irregularity and its implications for health. Annals of the New York Academy of Sciences 2001, 954:245-267.
4. Yeragani V.K., Sobolewski E., Jampala V.C., Kay J., Yeragani S., Igel G. Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues. Clinical Science 1998, 95:295-301.
5. Ning X., Xu Y., Wang J., Ma X. Approximate entropy analysis of short-term HFECG based on wave mode. Physica A 2005, 346:475-483.
6. Signorini M.G., Magenes G., Cerutti S., Arduini D. Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings. IEEE Transactions on Biomedical Engineering 2003, 50:365-374.
7. Chen W.T., Wang Z.Z., Ren X.M.X.M. Characterization of surface EMG signals using improved approximate entropy. Journal of Zhejiang University Science B 2006, 7:844-848.
8. Engoren M. Approximate entropy of respiratory rate and tidal volume during weaning from mechanical ventilation. Critical Care Medicine 1998, 26:1817-1823.
9. van den Berg G., Pincus S.M., Frölich M., Veldhuis J.D., Roelfsema F. Reduced disorderliness of growth hormone release in biochemically inactive acromegaly after pituitary surgery. European Journal of Endocrinology/European Federation of Endocrine Societies 1998, 138:164-169.
10. Schmitz O., Juhl C.B., Hollingdal M., Veldhuis J.D., Pørksen N., Pincus S.M. Irregular circulating insulin concentrations in type 2 diabetes mellitus: an inverse relationship between circulating free fatty acid and the disorderliness of an insulin time series in diabetic and healthy individuals. Metabolism: Clinical and Experimental 2001, 50:41-46.
11. Pincus S.M., Mulligan T., Iranmanesh A., Gheorghiu S., Godschalk M., Veldhuis J.D. Older males secrete luteinizing hormone and testosterone more irregularly, and jointly more asynchronously, than younger males. Proceedings of the National Academy of Sciences of the United States of America 1996, 93:14100-14105.
12. Burioka N., Miyata M., Cornélissen G., Halberg F., Takeshima T., Kaplan D.T., Suyama H., Endo M., Maegaki Y., Nomura T., Tomita Y., Nakashima K., Shimizu E. Approximate entropy in the electroencephalogram during wake and sleep. Clinical EEG & Neuroscience Journal 2005, 36:21-24.
13. Ferenets R., Lipping T., Anier A., Jäntti V., Melto S., Hovilehto S. Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Transactions on Biomedical Engineering 2006, 53:1067-1077.
14. Abásolo D., Hornero R., Espino P., Poza J., Sánchez C.I., de la Rosa R. Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy. Clinical Neurophysiology 2005, 116:1826-1834.
15. Hornero R., Escudero J., Fernández A., Poza J., Gómez C. Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer's disease. IEEE Transactions on Biomedical Engineering 2008, 55:1658-1665.
16. Radhakrishnan N., Gangadhar B.N. Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. IEEE Engineering in Medicine & Biology 1998, 17:89-94.
17. Srinivasan V., Eswaran C., Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions on Information Technology in Biomedicine 2007, 11:288-295.
18. Pincus S.M. Irregularity and asynchrony in biologic network signals. Methods in Enzymology 2000, 321:149-182.
19. Hudetz G., Wood J.D., Kampine J.P. Cholinergic reversal of isoflurane anesthesia in rats as measured by cross-approximate entropy of the electroencephalogram. Anesthesiology 2003, 99:1125-1131.
20. Licinio J., Negrão A.B., Mantzoros C., Kaklamani V., Wong M.L., Bongiorno P.B., Mulla A., Cearnal L., Veldhuis J.D., Flier J.S., McCann S.M., Gold P.W. Synchronicity of frequently sampled, 24-h concentrations of circulating leptin, luteinizing hormone, and estradiol in healthy women. Proceedings of the National Academy of Sciences of the United States of America 1998, 95:2541-2546.
21. Pincus S.M., Singer B.H. Randomness and degrees of irregularity. Proceedings of the National Academy of Sciences of the United States of America 1996, 93:2083-2088.
22. Vaillancourt D.E., Newell K.M. The dynamics of resting and postural tremor in Parkinson's disease. Clinical Neurophysiology 2000, 111:2046-2056.
23. Peupelmann J., Boettger M.K., Ruhland C., Berger S., Ramachandraiah C.T., Yeragani V.K., Bär K.J. Cardio-respiratory coupling indicates suppression of vagal activity in acute schizophrenia. Schizophrenia Research 2009, 112:153-157.
24. Álvarez D., Hornero R., Abásolo D., del Campo F., Zamarrón C., López M. Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnea syndrome. Physiological Measurement 2009, 30:967-982.
25. Owens J.D., Houston M., Luebke D., Green S., Stone J.E., Phillips J.C. GPU computing. Proceedings of the IEEE 2008, 96(5):879-899.
26. Owens J.D., Luebke D., Govindaraju N., Harris M., Krüger J., Lefohn A.E., Purcell T.J. A survey of general-purpose computation on graphics hardware. Proceedings of the Computer Graphics Forum 2007, 26(1):80-113.
27. Yang R., Welch G. Fast image segmentation and smoothing using commodity graphics hardware. Journal of Graphics Tools 2002, 7:91-100.
28. Fung J., Mann S. Using graphics devices in reverse: GPU-based image processing and computer vision. Proc. IEEE Multimedia and Expo 2008, 9-12.
29. Martínez-Zarzuela M., Díaz Pernas F.J., Díez Higuera J.F., Antón-Rodríguez M. Proc. International Workshop in Artificial Neural Networks. Lecture Notes in Computer Science 2007, 4507:463-470.
30. Gobron S., Devillard F., Heit B. Retina simulation using cellular automata and GPU programming. Machine Vision and Applications 2007, 18:331-342.
31. Ho T., Lam P., Leung C. Parallelization of cellular neural networks on GPU. Pattern Recognition 2008, 41:2684-2692.
32. Martínez-Zarzuela M., Díaz-Pernas F.J., Antón-Rodríguez M., Díez-Higuera J.F., González-Ortega D., Boto-Giralda D., López-González F., de la I., Torre-Díez Multi-scale neural texture classification using the GPU as a stream processing engine. Machine Vision and Applications 2011, 22:947-966.
33. Cecilia J.M., Nysbet A., Amos M., García J.M., Ujaldón M. Enhancing GPU parallelism in nature-inspired algorithms. Journal of Supercomputing 2012, 63:1-17.
34. Cates J.E., Lefohn A.E., Whitaker R.T. GIST: an interactive, GPU-based level set segmentation tool for 3D medical images. Medical Image Analysis 2004, 8:217-231.
35. Fluck O., Vetter C., Wein W., Kamen A., Preim B., Westermann R. A survey of medical image registration on graphics hardware. Computer Methods and Programs in Biomedicine 2011, 104:45-57.
36. Lee D., Dinov I., Dong B., Gutman B., Yanovsky I., Toga A.W. CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms. Computer Methods and Programs in Biomedicine 2012, 106:175-187.
37. Konstantinidis E.I., Frantzidis C.A., Pappas C., Bamidis P.D. Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units. Computer Methods and Programs in Biomedicine 2012, 107:16-27.
38. Wilson J.A., Williams J.C. Massively parallel signal processing using the graphics processing unit for real-time brain-computer interface feature extraction. Frontiers in Neuroengineering 2009, 2:1-13.
39. Chen D., Wang L., Ouyang G., Li X. Massively parallel neural signal processing on a many-core platform. Computing in Science and Engineering 2011, 13:42-51.
40. Liu Y., Schmidt B., Liu W., Maskell D.L. CUDA-MEME: accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units. Pattern Recognition Letters 2010, 31:2170-2177.
41. Veldhuis J.D., Pincus S.M., García-Rudaz M.C., Ropelato M.G., Escobar M.E., Barontini M. Disruption of the joint synchrony of luteinizing hormone, testosterone, and androstenedione secretion in adolescents with polycystic ovarian syndrome. Journal of Clinics in Endocrinology and Metabolism 2001, 86:72-79.
42. Richman J.S., Moorman J.R. Physiological time series analysis using approximate entropy and sample entropy. American Journal of Physiology. Heart and Circulatory Physiology 2000, 278:H2039-H2049.
43. CUDA C Best Practices Guide 2012, Nvidia Corporation, Santa Clara, CA.
44. Kirk D.B., Hwu W.W. Programming Massively Parallel Processors: A Hands-On Approach 2010, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
45. Cohen J., Garland M. Novel architectures: solving computational problems with GPU computing. Computing in Science and Engineering 2009, 11:58-63.
46. Brodtkorb A.R., Hagen T.R., Sætra M.L. Graphics processing unit (GPU) programming strategies and trends in GPU computing. Journal of Parallel and Distributed Computing 2013, 73:4-13.
47. Horn D. Stream reduction operations for GPGPU applications. GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation 2005, Addison-Wesley Professional, Upper Saddle River, NJ, (chapter 36). M. Pharr, R. Fernando (Eds.).
48. Harris M., Blelloch G.E., Maggs B.M., Govindaraju N.K., Lloyd B., Wang W., Lin M., Manocha D., Smolarkiewicz P.K., Margolin L.G., Smolarkiewicz P.K., Mikushin D.N., Stepanenko V.M., Kirk D.B., Hwu W.W., Kanter D. Optimizing parallel reduction in CUDA. Proceedings of the ACM SIGMOD 2007, 21(13):104-110.
49. Harris M., Sengupta S., Owens J.D. Parallel prefix sum (scan) with CUDA. GPU Gems 3: Programming Techniques for High-Performance Graphics and General-Purpose Computation 2007, Addison-Wesley, Professional, Boston, (chapter 39). H. Nguyen (Ed.).
50. Anokhin A.P., Birbaumer N., Lutzenberger W., Nikolaev A., Vogel F. Age increases brain complexity. Electroencephalography and Clinical Neurophysiology 1996, 99:63-68.
51. Dustman R.E., Shearer D.E., Emmerson R.Y. Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency. Clinical Neurophysiology 1999, 110:1399-1409.
52. John E.R., Ahn H., Prichep L. Developmental equations for the electroencephalogram. Science 1980, 210:1255-1258.
53. Meyer-Lindenberg A. The evolution of complexity in human brain development: an EEG study. Electroencephalography and Clinical Neurophysiology 1996, 99:405-411.
54. Smit D.J., Boersma M., Schnack H.G., Micheloyannis S., Boomsma D.I., Hulshoff Pol H.E., Stam C.J., de Geus E.J. The brain matures with stronger functional connectivity and decreased randomness of its network. PLoS ONE 2012, 7:e36896.
55. Fernández A., Zuluaga P., Abásolo D., Gómez C., Serra A., Méndez M.A., Hornero R. Brain oscillatory complexity across the life span. Clinical Neurophysiology 2012, 123:2154-2162.
56. Xie H.B., Guo J.Y., Zheng Y.P. A comparative study of pattern synchronization detection between neural signals using different cross-entropy measures. Biological Cybernetics 2010, 102:123-135.