Feature extraction across individual time series observations with spikes using wavelet principal component analysis

Statistics in Medicine

Volumn 32, Issue 21, 2013, Pages 3660-3669

  Røislien, Jo ^a,b   Winje, Brita ^a  

^a NORWEGIAN INSTITUTE OF PUBLIC HEALTH   (Norway)

^b UNIVERSITY OF OSLO   (Norway)

Author keywords

Fetal movement; Multiple imputation; Principal component analysis; Spikes; Temporal observations; Wavelets

Indexed keywords

ARTICLE; BODY MASS; CORRELATION COEFFICIENT; COVARIANCE; FETUS MOVEMENT; FETUS OUTCOME; HUMAN; MATERNAL AGE; METHODOLOGY; OUTCOME ASSESSMENT; PARITY; PREGNANCY OUTCOME; PRINCIPAL COMPONENT ANALYSIS; SIGNAL NOISE RATIO; SPIKE WAVE; TIME SERIES ANALYSIS; WAVELET ANALYSIS;

FETAL MOVEMENT; MULTIPLE IMPUTATION; PRINCIPAL COMPONENT ANALYSIS; SPIKES; TEMPORAL OBSERVATIONS; WAVELETS;

CLINICAL TRIALS AS TOPIC; DATA INTERPRETATION, STATISTICAL; FEMALE; FETAL MOVEMENT; FETUS; HUMANS; LONGITUDINAL STUDIES; PREGNANCY; PRINCIPAL COMPONENT ANALYSIS; TIME FACTORS;

EID: 84883135525     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5797     Document Type: Article

References (52)

1. Frøen JF. A kick from within - fetal movement counting and the cancelled progress in antenatal care. Journal of Perinatal Medicine 2004; 32:13-24.
2. Heazell AE, Frøen JF. Methods of fetal movement counting and the detection of fetal compromise. Journal of Obstetrics & Gynaecology 2008; 28:147-154.
3. Winje BA, Saastad E, Gunnes N, Tveit JVH, Stray-Pedersen B, Flenady V, Frøen JF. Analysis of 'count-to-ten' fetal movement charts: a prospective cohort study. BJOG: An International Journal of Obstetrics & Gynaecology 2011; 118(10):1229-1238.
4. Mangesi L, Hofmeyr GJ. Fetal movement counting for assessment of fetal wellbeing. Cochrane Database of Systematic Reviews 2007:CD004909. DOI: 10.1002/14651858.CD004909.pub2.
5. Frøen JF, Heazell AEP, Tveit JVH, Saastad E, Fretts RC, Flenady V. Fetal movement assessment. Seminars in Perinato-logy 2008; 32(4):243-246.
6. Ramsay JO, Silverman BW. Functional Data Analysis. Springer: New York, 2005.
7. Winje BA, Roislien J, Froen JF. Temporal patterns in count-to-ten fetal movement charts and their associations with pregnancy characteristics: a prospective cohort study. BMC Pregnancy and Childbirth 2012; 12(1):124.
8. Vidakovic B. Statistical Modeling by Wavelets. Wiley: New York, 1999.
9. Nason GP. Wavelet Methods in Statistics with R. Springer: New York, 2008.
10. Mahmoodabadi SZ, Ahmadian A, Abolhasani MD. ECG feature extraction using Daubechies wavelets. Proceeding of the Fifth IASTED International Conference, 2005; 343-348.
11. Englehart K, Hudgins B, Parker PA, Stevenson M. Classification of the myoelectric signal using time-frequency based representations. Medical engineering & Physics 1999; 21(6):431-438.
12. Englehart K, Hudgins B, Parker P, Stevenson M. Time-frequency representation for classification of the transient myoelectric signal. Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, Vol.5, 2001; 2627-2630.
13. Pavlov A, Makarov V, Makarova I, Panetsos F. Sorting of neural spikes: when wavelet based methods outperform principal component analysis. Natural Computing 2007; 6(3):269-281.
14. Yang X, Shamma SA. A totally automated system for the detection and classification of neural spikes. IEEE Transactions on Biomedical Engineering 1988; 35(10):806-816.
15. Nenadic Z, Burdick JW. Spike detection using the continuous wavelet transform. IEEE Transactions on Biomedical Engineering 2005; 52(1):74-87.
16. Morris JS, Carroll RJ. Wavelet-based functional mixed models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2006; 68(2):179-199.
17. Malloy EJ, Morris JS, Adar SD, Suh H, Gold DR, Coull BA. Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models. Biostatistics 2010; 11(3):432-452.
18. Vidakovic B. Wavelet-based functional data analysis: theory, applications and ramifications. In Proceedings of The 3rd Pacific Symposium on Flow Visualization and Image Processing, Kobayashi T. (ed.), 2001.
19. Pigoli D, Sangalli LM. Wavelets in functional data analysis: estimation of multidimensional curves and their derivatives. Computational Statistics & Data Analysis 2012; 56(6):1482-1498.
20. Mardia KV, Kent JT, Bibby J. Multivariate Analysis. Academic Press: London, 1979.
21. Jolliffe IT. Principal Component Analysis. Springer-Verlag: New York, 2002.
22. Gupta MR, Jacobson NP. Wavelet principal component analysis and its application to hyperspectral images. 2006 IEEE International Conference on Image Processing, 8-10-2006; 1585-1588.
23. Kosanovich KA, Piovoso MJ. PCA of wavelet transformed process data for monitoring. Intelligent Data Analysis 1997; 1(1-4):85-99.
24. Jung YY, Park Y, Jones DP, Ziegler TR, Vidakovic B. Self-similarity in NMR Spectra: an application in assessing the level of cysteine. Journal of Data Science: JDS 2010; 8(1):1-19.
25. Strang G. Wavelets and dilation equations: a brief introduction. SIAM Review 1989; 31(4):614-627.
26. Flanders M. Choosing a wavelet for single-trial EMG. Journal of Neuroscience Methods 2002; 116(2):165-177.
27. Farge M. Wavelet transforms and their applications to turbulence. Annual Review of Fluid Mechanics 1992; 24(1):395-458.
28. Strong SP, Koberle R, de Ruyter van Steveninck R, Bialek BW. Entropy and information in neural spike trains. Physical Review Letters 1998; 80(1):197-200.
29. Daubechies I. Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics: Philadelphia, 1992.
30. Donoho DL. De-noising by soft-thresholding. IEEE Transactions on Information Theory 1995; 41(3):613-627.
31. Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994; 81(3):425-455.
32. Chipman HA, Kolaczyk ED, McCulloch RE. Adaptive Bayesian wavelet shrinkage. Journal of the American Statistical Association 1997; 92(440):1413-1421.
33. Johnstone IM, Silverman BW. Needles and straw in haystacks: empirical Bayes estimates of possibly sparse sequences. The Annals of Statistics 2004; 32(4):1594-1649.
34. Johnstone IM, Silverman BW. EbayesThresh: R programs for empirical Bayes thresholding. Journal of Statistical Software 2005; 12(8):1-38.
35. Johnstone I, Silverman B. Empirical Bayes selection of wavelet thresholds. The Annals of Statistics 2005; 33(4):1700-1752.
36. Abramovich F, Sapatinas T, Silverman BW. Wavelet thresholding via a Bayesian approach. Journal of the Royal Statistical Society.Series B (Statistical Methodology) 1998; 60(4):725-749.
37. Pearson K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 1901; 2(6):559-572.
38. Rubin DB. Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of the American Statistical Association 1977; 72(359):538-543.
39. Rubin DB. Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987.
40. Freedman VA, Wolf DA. A case study on the use of multiple imputation. Demography 1995; 32(3):459-470.
41. Schafer JL. Analysis of Incomplete Multivariate Data. Chapman & Hall: London, 1997.
42. Schafer JL. Multiple imputation: a primer. Statistical Methods in Medical Research 1999; 8(1):3-15.
43. Honaker J, King G. What to do about missing values in time-series cross-section data. American Journal of Political Science 2010; 54(2):561-581.
44. King G, Honaker J, Joseph A, Scheve K. Analyzing incomplete political science data: an alternative algorithm for multiple imputation. The American Political Science Review 2001; 95(1):49-69.
45. Schafer J, Olsen M. Multiple imputation for multivariate missing-data problems: a data analyst's perspective. Multivariate Behavioral Research 1998; 33(4):545-571.
46. King G, Tomz M, Jason W. Making the most of statistical analyses: improving interpretation and presentation. American Journal of Political Science 2000; 44(2):347-361.
47. Fiori S, Tanaka T. An algorithm to compute averages on matrix Lie groups. IEEE Transactions on Signal Processing 2009; 57(12):4734-4743.
48. Higham NJ. Computing the nearest correlation matrix-a problem from finance. IMA Journal of Numerical Analysis 2002; 22(3):329-343.
49. Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989; 11(7):674-693.
50. Daubechies I, Sweldens W. Factoring wavelet transforms into lifting steps. The Journal of Fourier Analysis and Applications 1998; 4(3):247-269.
51. Allen J. Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Transactions on Acoustics, Speech and Signal Processing 1977; 25(3):235-238.
52. Allen JB, Rabiner LR. A unified approach to short-time Fourier analysis and synthesis. Proceedings of the IEEE 1977; 65(11):1558-1564.