



@article {2590,
	title = {Classification of foetal heart rate sequences based on fractal features},
	journal = {Medical and Biological Engineering and ComputingMed. Biol. Eng. Comput.},
	volume = {36},
	year = {1998},
	note = {Cited By (since 1996): 13Export Date: 21 November 2012Source: ScopusCODEN: MBECDdoi: 10.1007/BF02510743PubMed ID: 9684460Language of Original Document: EnglishCorrespondence Address: Felguiras, C.S.; Instituto de Engenharia Biomedica, Praca Coronel Pacheco 1, 4050 Porto, PortugalReferences: Bernardes, J., (1993) Leitura Automatizada Do Cardiotocograma. Desenvolvimento e Availia{\c c}{\`a}o, , Ph.D. thesis, Faculdade de Medicina da Universidade do Porto, Portugal;Bernardes, J., Moura, C., Marques De S{\'a}, J.P., Pereira Leite, L., Van Geijn, H.P., The Porto system (1994) A Critical Appraisal of Foetal Surveillance, pp. 315-324. , VAN GEIJN H. P. and COPRAY, F. J. A. (Eds.) Elsevier Science, Amsterdam; 
Chaffin, D.G., Goldberg, C.C., Reed, K.L., The dimension of chaos in foetal heart rate (1991) Am. J. Obstet. Gynecol., 165 (5), pp. 1425-1429; 
Dawes, G.S., Visser, G.H.A., Goodman, J.D.S., Redman, C.W.G., Numerical analysis of human foetal heart rate: The quality of ultrasound records (1981) Am. J. Obstet, Gynecol., 141 (1), pp. 43-52; 
Felgueiras, C.S., Marques De S{\'a}, J.P., Bernardes, J., Gama, S., On the reconstruction of foetal heart rate sequences (1996) Actas de Bieng {\textquoteright}96, Sociedade Portuguesa de Engenharia Biom{\'e}dica, Aveiro, Mar{\c c}o, 1996, pp. VII61-VII65; 
Goldberger, A.L., Fractal mechanisms in the electrophysiology of the heart (1992) IEEE Eng. Med. Biol., pp. 47-52. , June; 
Grant, A., Principles for clinical evaluation of methods of perinatal monitoring (1984) J. Perinat. Med., 12, pp. 227-231; 
Grassberger, P., Procaccia, I., Measuring the strangeness of strange attractors (1983) Physica D, 9, pp. 189-208; 
Higushi, T., Approach to an irregular time series on the basis of the fractal theory (1988) Physica D, 31, pp. 277-283; 
Hoyer, D., Liebovitch, L.S., Schmidt, K., Luethke, B., Zwiener, U., Practical problems of determining the dimensions of heart rate data (1997) Med. Biol. Eng. Comput., 35, pp. 27-32; 
Maldelbrot, B.B., Van Ness, J.W., Fractional Brownian motions, fractional noises and applications (1968) SIAM Rev., 10 (4), pp. 422-437; 
Marques De Sa, J.P., Moura, A.C., Bernardes, J., Pereira Leite, L., An{\'a}lise autom{\'a}tica de sinais cardiotocogr{\'a}ficos (1992) Acta M{\'e}dica Portuguesa 11 S{\'e}rie, 5 (1), pp. 28-30; 
Parker, T.S., Chua, L.O., (1989) Practical Numerical Algorithms for Chaotic Systems, , Springer-Verlag; 
Schulman, H., Schulman, S., Lai, P., Schneider, E., Farmakides, G., Dawes, G., Antepartum computerized foetal heart rate, chaos analysis, blood gases and perinatal outcome (1994) J. Maternal-Fetal Invest., 4, pp. 59-63
},
	month = {1998///},
	pages = {197 - 201},
	publisher = {Peter Peregrinus Ltd},
	abstract = {Visual inspection of foetal heart rate (FHR) sequences is an important means of foetal well-being evaluation. The application of fractal features for classifying physiologically relevant FHR sequence patterns is reported. The use of fractal features is motivated by the difficulties exhibited by traditional classification schemes to discriminate some classes of FHR sequence and by the recognition that this type of signal exhibits features on different scales of observation, just as fractal signals do. To characterise the signals by fractal features, two approaches are taken. The first models the FHR sequences as temporal fractals. The second uses techniques from the chaos-theory field and aims to model the attractor based on FHR sequences. The fractal features determined by both approaches are used to design a Bayesian classification scheme. Classification results for three classes are presented; they are quite satisfactory and illustrate the importance of this type of methodology.Visual inspection of foetal heart rate (FHR) sequences is an important means of foetal well-being evaluation. The application of fractal features for classifying physiologically relevant FHR sequence patterns is reported. The use of fractal features is motivated by the difficulties exhibited by traditional classification schemes to discriminate some classes of FHR sequence and by the recognition that this type of signal exhibits features on different scales of observation, just as fractal signals do. To characterise the signals by fractal features, two approaches are taken. The first models the FHR sequences as temporal fractals. The second uses techniques from the chaos-theory field and aims to model the attractor based on FHR sequences. The fractal features determined by both approaches are used to design a Bayesian classification scheme. Classification results for three classes are presented; they are quite satisfactory and illustrate the importance of this type of methodology.
},
	keywords = {article, Bayes Theorem, Cardiology, Chaos techniques, Chaos theory, Classification, Computer aided analysis, Computer simulation, Electrocardiography, Female, Fetal monitoring, fetus, fetus heart rate, FHR classification, FHR sequence, Foetal heart rate sequences, fractal analysis, Fractal features, Fractals, Heart Rate, Fetal, human, Humans, Pattern recognition, Pregnancy, Signal Processing, Computer-Assisted, technique},
	isbn = {01400118 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0032033010\&partnerID=40\&md5=563144fcdb064dd777002155aaa62f73},
	author = {Felgueiras, C.S. and De Marques S{\'a}, J.P. and Bernardes, J. and Gama, S.}
}
