| Title | Classification of foetal heart rate sequences based on fractal features |
| Publication Type | Journal Article |
| 1998 |
| Authors | Felgueiras, CS, De Marques Sá, JP, Bernardes, J, Gama, S |
| Journal | Medical and Biological Engineering and ComputingMed. Biol. Eng. Comput. |
| Volume | 36 |
| Issue | 2Stevenage, United Kingdom |
| Pagination | 197 - 201 |
| Date Published | 1998/// |
| 01400118 (ISSN) |
| 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 |
| 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.
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