Neural network analysis and evaluation of the fetal heart rate

Algorithms

Volumn 2, Issue 1, 2009, Pages 19-30

  Noguchi, Yasuaki ^a   Matsumoto, Fujihiko ^a   Maeda, Kazuo ^b   Nagasawa, Takashi ^c  

^a NATIONAL DEFENSE ACADEMY   (Japan)

^b Professor Emeritus   (Japan)

^c Department of Information Technology   (Japan)

Author keywords

Fetal heart rate (FHR); Fetus; Neonatal asphyxia; Neonate; Neural network; Nonreassuring fetal status; Sinusoidal FHR

Indexed keywords

EID: 77749262214     PISSN: None     EISSN: 19994893     Source Type: Journal    
DOI: 10.3390/a2010019     Document Type: Review

References (42)

1. Maeda, K. Fetal heart sound recorded by using slow down tape technique. In Proc. 8th Int. Conf. Med. Biol. Eng., 1969; Session 19-2.
2. Symonds, E.M. Configuration of the fetal electrocardiogram in relation to the fetal acid-base balance and plasma electrolytes. J. Obstetet. Gynaecol. Brit. Commonw. 1971, 78, 957-970.
3. Gentner, O.; Hammacher, K. An improved method for the determination of the instantaneous fetal heart frequency from the fetal phonocardiogram. In Proc. 7th Int. Conf. Med. Biol. Eng., 1967; No. 140.
4. Maeda, K.; Ezaki, I. Fetal cardiotachography recorded with fetal heart sound during pregnancy and labor. In Proc. 7th Int. Conf. Med. Biol. Eng., 1967; No. 144.
5. Hammacher, K. Einführung in die Cardiotokographie. 5. Teil: Die Herzfrequenzmessung mit US = Ultraschall. Die Schweizer Hebamme 1976, 74, 17-21 (in German).
6. Takeuchi, Y.; Hogaki, N. Auto correlation method for fetal heart rate measurement from ultrasonic Doppler fetal signal. In Ultrasound in Medicine; White, D., Brown, R.E., Eds.; Plenum Press: New York, 1977; Vol. 3B " Engineering Aspect" , pp. 1327-1332.
7. Tsuzaki, T.; Sekijima, A.; Morishita, K.; Takeuchi, Y.; Mizuta, M.; Minagawa, Y.; Nakajima, K.; Maeda, K. Survey on the perinatal variables and the incidence of cerebral palsy for 12 years before and after the application of the fetal monitoring system. Nippon Sanka Fujinka Gakkai Zasshi 1990, 42, 99-105.
8. Takeshita, K.; Ando, Y.; Ohtani, K.; Takashima, S. Cerebral palsy in Tottori, Japan. Benefits and risks of progress in perinatal medicine. Neuroepidemiology 1989, 4, 184-192.
9. Maeda, K.; Arima, T.; Tatsumura, M.; Nagasawa, T. Computer-aided fetal heart rate analysis and automatic fetal-distress diagnosis during labor and pregnancy utilizing external techniques in fetal monitoring. MEDINFO 1980, 80, 1214-1219.
10. Dawes, G.S.; Moulden, M.; Redman, C.W. System 8000: computerized antenatal FHR analysis. J. Perinat. Med. 1991, 19, 47-51.
11. Bernardes, J.; Moura, C.; de Sa, J.P.; Leite, L.P. The Porto system for automated cardiotocogaphic signal analysis. J. Perinat. Med. 1991, 19, 61-65.
12. Keith, R.D.; Westgate, J.; Ifeachor, E.C.; Greene, K.R. Suitability of artificial neural networks for feature extraction from cardiotocogram during labor. Med. Biol. Eng. Comput. 1994, 32, S51-S57.
13. Ulbricht, C.; Dorfner, G.; Lee, A. Neural networks for recognizing patterns in cardiotocograms. Artif. Intell. Med. 1998, 12, 271-284.
14. Kupka, T.; Wrobel, J.; Jerewski, J.; Gacek, A.; Jerewski, M. Evalation of fetal heart rate baseline estimation method using testing signals based on a statistical model. Proc. IEEE Eng. Med. Biol. Soc. 2006, 1, 3728-3731.
15. Jezewski, M.; Wrobel, J.; Labaj, P.; Leski, J.; Henzel, N.; Horoba, K.; Jezewski, J. Some practical remarks on neural networks approach to fetal cardiotocograms classification. Proc. IEEE Eng. Med. Biol. Soc. 2007, 5170-5173.
16. Maeda, K.; Noguchi, Y.; Matsumoto, F.; Nagasawa, T. Quantitative fetal heart rate evaluation without pattern classification: FHR score and artificial neural network analysis. In Textbook of Perinatal Medicine, 2nd Ed.; Kurjak, A., Chervenak, F.A., Eds.; Informa: London, UK, 2006; Vol. 2, pp. 1487-1495.
17. Maeda, K. Computerized automatic diagnosis of fetal distress with the use of external monitoring technique. In Computerdiagnostik in der Geburtsmedizin. Krause, W., Ed.; Friedrich-Schiller-Universität: Jena, Germany, 1981; pp. 28-49.
18. Ito, T.; Maeda, K.; Takahashi, H.; Nagata, N.; Nakajima, K.; Terakawa, N. Differentiation between physiologic and pathologic sinusoidal FHR pattern by fetal actocardiogram. J. Perinat. Med. 1994, 221, 39-43.
19. Maeda, K.; Nagasawa, T. Automatic computerized diagnosis of fetal sinusoidal heart rate. Fetal Diag. Ther. 2006, 20, 328-334.
20. Rumelhart, D.E.; McClelland, J.L. Parallel Distributed Processing; The MIT Press: Cambridge, MA, 1986; Vol. 1 & 2.
21. Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning Internal Representations by Error Propagation. Parallel Distributed Processing; The MIT Press: Cambridge, MA, 1986; Vol. 1, pp. 318-362.
22. Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning Representations by Back-Propagating Errors. Nature 1986, 323, 533-536.
23. Maeda, K.; Utsu, M.; Makio, A.; Serizawa, M.; Noguchi, Y.; Hamada, T.; Mariko, K,; Matsumoto, F. Neural network computer analysis of fetal heart rate. J. Matern. Fetal Invest. 1998, 8, 163-171.
24. Maeda, K.; Kimura, S.; Fukui, Y,; Ozawa, S.; Kosaka, T.; Wang, C.F.M.; Tamura, M.; Takata, D.; Nakano, H.; Mitoma, M. Pathophysiology of Fetus; Fukuoka Printing: Fukuoka, Japan, 1969.
25. Irie, T. Automated fetal heart rate analysis and its trend gram in relation to the gas analysis and acid-base balance of umbilical arterial blood. Nippon Sanka Fujinka Gakkai Zasshi 1986, 38, 1623-1631.
26. Maeda, K.; Noguchi, Y.; Matsumoto, F. Evaluation of prolonged fetal monitoring with normal and pathologic outcome probabilities determined by artificial neural network. Fetal Diag. Ther. 2003, 18, 370-376.
27. Tokuda, I.; Nagashima, T.; Aihara, K. Global bifurcation structure of chaotic neural networks and its application to traveling salesman problems. Neural Networks 1997, 10, 1673-1690.
28. Weng, W.; Khorasani, K. An adaptive structure neural networks application to EEG automatic seizure detection. Neural Networks 1996, 9, 1223-1240.
29. Moul, J.W.; Snow, P.H.; Fernandez, E.B.; Maher, P.D.; Sesterhenn, I.A. Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer. J. Urol. 1995, 153, 1674-1677.
30. Ruppin, E.; Reggia, J.A. A neural model of memory impairment in diffuse cerebral atrophy. Br. J. Psychiatry 1995, 166, 19-28.
31. Arle. J.E.; Moriss, C.; Wang, Z.J.; Zimmerman, R.A.; Philipps, P.G.; Sutton, L.N. Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks. J. Neurosurg. 1997, 86, 755-761.
32. Brickley, M.R.; Cowpe, J.G.; Shepherd, J.P. Performance of a computer-simulated neural network trained to categorize normal, premalignant and malignant oral smears. J. Oral. Pathol. Med. 1996, 25, 424-428.
33. Mutlukan, E.; Keating, D. Visual field interpretation with a personal computer-aided neural network. Eye 1994, 8, 321-323.
34. Westenskow, D.R.; Orr, J.A.; Simon, F.H.; Bender, H.J.; Frankenberger, H. Intelligent alarms reduce anesthesiologist's response time to critical faults. Anesthesiology 1992, 77, 1074-1079.
35. Fujita, H.; Katafuchi, T.; Uehara, T.; Nishimura, T. Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull's eye images. J. Nucl. Med. 1992, 33, 272-276.
36. Andreassen, H.; Bohr, H.; Bohr, J.: Brunak, S.: Bugge, T. Analysis of the secondary structure of the human immunodeficiency virus (HIV) proteins p17, gp120 and gp41 by computer modeling based on neural network methods. J. Acquir. Immune Def. Syndr. 1990, 3, 615-622.
37. De Voe, L.D.; Samuel, S.; Prescott, P.; Work, B.A. Predicting the duration of the first stage of spontaneous labor using a neural network. J. Mat. Fet. Med. 1996, 5, 256-261.
38. Farmer, R.M.; Medearis, A.L.; Hirata, G.I.; Platt, L.D. The use of a neural network for the ultrasonographic estimation of fetal weight in the macrosomic fetus. Am. J. Obstet. Gynecol. 1992, 166, 1467-1472.
39. Ferrario, M.; Signorini, M.G.; Magenes, G. Comparison of entropy-bases regularity estimators: application to the fetal heart rate signal for the identification of fetal distress. IEEE Trans. Biomed. Eng. 2006, 53, 119-125.
40. Cattani, C.; Doubrovina, O.; Rogosin, S.; Voskresensky, S.L.; Zelianko E. On the creation of a new diagnostic model for fetal well-being on the base of wavelet analysis of cardiotocograms. J. Med. Sys. 2006, 30, 489-494.
41. Al-Angari, H.M.; Sahakian, A.V. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans. Biomed. Eng. 2007, 54, 1900-1904.
42. Assaleh, K. Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems. IEEE Trans. Biomed. Eng. 2007, 54, 59-68.