Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

IEEE Transactions on Biomedical Engineering

Volumn 53, Issue 5, 2006, Pages 875-884

  Georgoulas, George ^b   Stylios, Chrysostomos D ^a,b,c,d   Groumpos, Peter P ^a,b,e,f,g,h  

^a IEEE   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

^c Epirus Institute of Technology Arta   (Greece)

^d National Technical Chamber of Greece   (Greece)

^e National Technical Chamber of Greece ^*  (United States)

^f Laboratory for Automation and Robotics   (Greece)

^g IST Program   (Belgium)

^h IFAC   (Italy)

Author keywords

Feature extraction; Fetal heart rate (FHR); Intrapartum monitoring; Metabolic acidosis; Support vector machines (SVMs)

Indexed keywords

FETAL HEART RATE (FHR); INTRAPARTUM MONITORING; METABOLIC ACIDOSIS; SUPPORT VECTOR MACHINES (SVMS);

FEATURE EXTRACTION; FETAL MONITORING; FREQUENCY DOMAIN ANALYSIS; HEALTH RISKS; METABOLISM;

CARDIOLOGY;

ARTICLE; ARTIFACT; CARDIOTOCOGRAPHY; CLINICAL PRACTICE; FETUS HEART RATE; FETUS MONITORING; METABOLIC ACIDOSIS; RISK ASSESSMENT; SIGNAL PROCESSING; SUPPORT VECTOR MACHINE; THEORY;

ACIDOSIS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CARDIOTOCOGRAPHY; CLUSTER ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; HEART RATE, FETAL; HUMANS; INFANT, NEWBORN; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 33646338651     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2006.872814     Document Type: Article

References (63)

1. J. T. Parer, Handbook of Fetal Heart Rate Monitoring, 2nd ed. Philadelphia, PA: Saunders, 1997.
2. H. P. van Geijn, "Developments in CTG analysis," Bailliere's Clin. Obstet. Gynaecol., vol. 10, no. 2, pp. 185-209, Jun. 1996.
3. D. MacDonald, A. Grant, M. Sheridan-Pereira, P. Boylan, and I. Chalmers, "The Dublin randomized controlled trial of intrapartum fetal heart rate monitoring," Am. J. Obstet. Gynecol., vol. 152, no. 5, pp. 524-539, Jul. 1985.
4. J. Bernardes, A. Costa-Pereira, D. Ayres-de-Campos, H. P. van Geijn, and L. Pereira-Leite, "Evaluation of interobserver agreement of cardiotocograms," Int. J. Gynecol. Obset., vol. 57, no. 1, pp. 33-37, 1997.
5. D. Ayres-de-Campo, J. Bernardes, A. Costa-Pereira, and L. Pereira-Leite, "Inconsistencies in expert's classification of cardiotocograms and subsequent clinical decision," Br. J. Obstet. Gynaecol., vol. 106, pp. 1307-1310, 1999.
6. G. Rooth, A. Huch, and R. Huch, "Guidelines for the use of fetal monitoring," Int. J. Gynaecol. Obstet., vol. 25, pp. 159-167, 1987.
7. National Institute of Child Health and Human Development Research Planning Workshop, "Electronic fetal heart rate monitoring: research guidelines for interpretation," Am. J. Obstet. Gynecol., vol. 177, no. 5, pp. 1385-1390, Dec. 1997.
8. M. G. Signorini, G. Magenes, S. Cerutti, and D. Arduini, "Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings," IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 365-374, Mar. 2003.
9. D. Arduini, G. Rizzo, G. Piana, A. Bonalumi, P. Brambilla, and C. Romanini, "Computerized analysis of fetal heart rate: I. Description of the system (2CTG)," J. Matern.. Fetal Invest., vol. 3, pp. 159-163, 1993.
10. R. Mantel, H. P. van Geijn, F. J. M. Caron, J. M. Swartjes, E. E. van Woerden, and H. W. Jongsma, "Computer analysis of antepartum fetal heart rate: 1. Baseline determination," Int. J. Biomed. Comput., vol. 25, no. 4, pp. 261-272, May 1990.
11. _, "Computer analysis of antepartum fetal heart rate: 2. Detection of accelerations and decelerations," Int. J. Biomed. Comput., vol. 25, no. 4, pp. 273-286, May 1990.
12. M. Mongelli, R. Dawkins, T. Chung, D. Sahota, J. A. D. Spencer, and A. M. Z. Chang, "Computerized estimation of the baseline fetal heart rate in labour: the low frequency line," Br. J. Obstet. Gynaecol., vol. 104, no. 10, pp. 1128-1133, Oct. 1997.
13. G. M. Taylor, G. J. Mires, E. W. Abel, S. Tsantis, T. Farrell, P. F. W. Chien, and Y. Liu, "The development and validation of an algorithm for real time computerized fetal heart rate monitoring in labour," Br. J. Obstet. Gynaecol., vol. 107, pp. 1130-1137, Sep. 2000.
14. G. S. Dawes, M. Moulden, and C. W. Redman, "Computerized analysis of antepartum fetal heart rate," Am. J. Obstet. Gynecol., vol. 173, no. 4, pp. 1353-1354, 1995.
15. J. Jezewski and J. Wrobel, "Foetal monitoring with automated analysis of cardiotocogram: the KOMPOR system," in Proc. 15th Ann. Card. IEEE/EMBS, San Diego, CA, 1993, pp. 638-639.
16. K. Maeda, "Computerized analysis of cardiotocograms and fetal movements," Bailliere's Clin. Obstet. Gynaecol., vol. 4, no. 4, pp. 1797-1813, Dec. 1990.
17. C. Ulbricht, G. Dorffner, and A. Lee, "Neural networks for recognizing patterns in cardiotocograms," Artif. Intell. Med., vol. 12, pp. 271-284, 1998.
18. O. Fontenla-Romero, A. Alonso-Betanzos, and B. Guijarro-Berdinas, "Adaptive pattern recognition in the analysis of cardiotocographic records," IEEE Trans. Neural Netw., vol. 12, no. 5, pp. 1188-1195, Sep. 2001.
19. J. Bernardes, C. Moura, J. P. M. de Sa, and L. Pereira-Leite, "The Porto system for automated cardiotocographic signal analysis," J. Perinat. Med., vol. 19, pp. 61-65, 1991.
20. J. Bernardes, C. Moura, J. P. M. de Sa, L. Pereira-Leite, and H. P. van Geijn, "The Porto system," in A Critical Appraisal of Fetal Surveillance, H. P. van Geijn and F. J. A. Copray, Eds. New York: Elsevier Science, 1994, pp. 315-324.
21. D. Ayres-de-Campos, J. Bernardes, A. Garrido, J. P. M. de Sa, and L. Pereira-Leite, "SisPorto 2.0: a program for automated analysis of cardiotocograms," J. Matern.. Fetal Med., vol. 9, pp. 311-318, 2000.
22. G. Magenes, M. G. Signorini, and D. Arduini, "Classification of cardiotocographic records by neural networks Neural Networks," in Proc. IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks (IJCNN'00), 2000, vol. 3, pp. 637-641.
23. _, "Multiparametric analysis of fetal heart rate: comparison of neural and statistical methods," in Proc. Medicon 2001, pp. 360-363.
24. S. Kol and I. Thaler, "Interpretation of nonstress tests by an artificial neural network," Am. J. Obstet. Gynecol., vol. 172, no. 5, pp. 1372-1379, May 1995.
25. T. K. H. Chung, M. P. Mohajer, X. J. Yang, A. M. Z. Chang, and D. S. Sahota, "The prediction of fetal acidosis at birth by computerized analysis of intrapartum cardiotocography," Br. J. Obstet. Gynaecol., vol. 102, pp. 454-460, Jun. 1995.
26. E. Salamalekis, P. Thomopoulos, D. Giannaris, I. Salloum, G. Vasios, A. Prentza, and D. Koutsouris, "Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks," Br. J. Obstet. Gynaecol., vol. 109, no. 10, pp. 1137-1142, Oct. 2002.
27. Z. R. Struzik and W. J. van Wijngaarden, Cumulative Effective Hölder Exponent Based Indicator for Real Time Fetal Heartbeat Analysis During Labour Rep. INS-R0110, Nov. 2001.
28. E. C. Ifeachor, R. D. F. Keith, J. Westgate, and K. R. Greene, "An expert system to assist in the management of labour," in Proc. World Congr. Expert Systems, 1991, vol. 4, pp. 2615-2622.
29. J. F. Skinner, J. M. Garibaldi, and E. C. Ifeachor, "A fuzzy system for fetal heart rate assessment," in Proc. 6th Fuzzy Days on Computational Intelligence, Dortmund, Germany, 1999, pp. 20-29.
30. A. Alonso-Betanzos, V. Moret-Bonillo, L. D. Devoe, J. R. Searle, B. Banias, and E. Ramos, "Computerized antenatal assessment: the NST-EXPERT project," Automedica, vol. 14, pp. 3-22, 1992.
31. A. Alonso-Betanzos, B. Guijarro-Berdinas, V. Moret-Bonillo, and S. Lopez-Gonzalez, "The NST-EXPERT project: the need to evolve," Artif. Intell. Med., vol. 7, no. 4, pp. 297-313, 1995.
32. B. Guijarro-Berdinas, A. Alonso-Betanzos, and O. Fontenla-Romero, "Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system," Artif. Intell., vol. 136, pp. 1-27, 2002.
33. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
34. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
35. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
36. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, "An introduction to kernel-based learning algorithms," IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 181-201, Mar. 2001.
37. H. Byun and S. W. Lee, "Application of support vector machines for pattern recognition: A survey," in Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag, 2002, vol. 2388, Proc. 1st Int. Workshop SVM 2002, pp. 213-236.
38. K. Veropoulos, N. Cristianini, and C. Campbell, "The application of support vector machines to medical decision support: a case study," in Advanced Course in Artificial Intelligence (ACAI'99), Jul. 1999.
39. R. P. W. Duin, "Classifiers in almost empty spaces," in Proc. 15th International Conference on Pattern Recognition (ICPR'00), Barcelona, Spain, Sep. 3-8, 2000, vol. 2, pp. 1-7.
40. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge Univ. Press, 2000.
41. E. E. Osuna, R. Freund, and F. Girosi, Support Vector Machines: Training and Applications MIT, A.I. Memo no. 1602, March 1997.
42. K. Veropoulos, C. Cambell, and N. Cristianini, "Controlling the sensitivity of support machines," in Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI99), Stockholm, Sweden, 1999, pp. 55-60.
43. M. C. Carter, "Present-day performance qualities of cardiotocographs," Br. J. Obstet, Gynaecol., vol. 100, pp. 10-14, Mar. 1993, suppl. 9.
44. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, "Heart rate variability. Standards of measurements, physiological interpretation, and clinical use," Eur. Heart J., vol. 17, no. 3, pp. 354-381, 1996.
45. E. N. Bruce, Biomedical Signal Processing and Signal Modeling. New York: Wiley, 2001.
46. O. Sibony, J. P. Fouillot, M. Benaudia, A. Benhalla, P. Blot, and C. Sureau, "Spectral analysis: a method for quantitating fetal heart rate variability," in A Critical Appraisal of Fetal Surveillance, H. P. van Geijn and F. J. A. Copray, Eds. New York: Elsevier Science, 1994, pp. 325-332.
47. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
48. K. K. Lee, S. R. Gunn, C. J. Harris, and P. A. S. Reed, "Classification of imbalanced data with transparent kernels," in Proc. INNS-IEEE Int. Joint Conf. Neural Networks, 2001, pp. 2410-2415.
49. S. Perkins, N. R. Harvey, S. P. Brumby, and K. Lacker, "Support vector machines for broad area feature extraction in remotely sensed images," Proc. SPIE, vol. 4381, Apr. 2001.
50. R. Akbani, S. Kwek, and N. Japkowicz, "Applying support vector machines to imbalanced datasets," in Proc. Eur. Conf. Machine Learning (ECML'2004), Pisa, 2004, pp. 39-50.
51. M. F. Docker, "Doppler ultrasound monitoring technology," Br. J. Obstet. Gynaecol., vol. 100, pp. 454-460, Mar. 1993, suppl. 9.
52. R. D. Berger, S. Akselrod, D. Gordon, and R. J. Cohen, "An efficient algorithm for spectral analysis of heart rate variability," IEEE Trans. Biomed. Eng., vol. 33, no. 9, pp. 900-904, Sep. 1986.
53. M. Bracale, M. Romano, M. Cesareli, P. Bifulco, and M. Sansone, "Cardiotocographic data pre-processing and AR modeling of fetal heart rate signals," presented at the World Congress on Medical Physics and Biomedical Engineering, Sydney, Australia, Aug. 24-29, 2003, Paper 3363, unpublished.
54. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth Int. Group, 1984.
55. S. L. Salzberg, "On comparing classifiers: pitfalls to avoid and a recommended approach," Data Mining Knowledge Discovery, vol. 1, pp. 317-328, 1997.
56. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
57. M. Kubat and S. Matwin, "Addressing the curse of imbalanced data sets: One-sided sampling," in Proc. 14th Int. Conf. Machine Learning, 1999, pp. 179-186.
58. F. Provost and T. Fawcett, "Analysis and visualization of classifier performance: comparison under imprecise class and cost distribution," in Proc. 3rd Int. Conf. Knowledge Discovery and Data Mining, 1997, pp. 43-48.
59. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognit., vol. 30, no. 7, pp. 1145-1159, 1997.
60. G. S. Dawes, "Computerized fetal heart rate analysis," in A Critical Appraisal of Fetal Surveillance, H. P. van Geijn and F. J. A. Copray, Eds. New York: Elsevier Science, 1994, pp. 311-314.
61. J. Bernardes, D. Ayres-de-Campos, A. Costa-Pereira, L. Pereira-Leite, and A. Garrido, "Objective computerized fetal heart rate analysis," Int. J. Gynecol. Obset., vol. 62, pp. 141-147, 1998.
62. B. K. Strachan, D. S. Sahota, W. J. van Wijngaarden, D. K. James, and A. M. Z. Chang, "Computerised analysis of the fetal heart rate and relation to acidaemia at delivery," Int. J. Obstet. Gynaecol., vol. 108, no. 8, pp. 848-852, August 2001.
63. G. Georgoulas, G. Nokas, C. Stylios, and P. P. Groumpos, "Classification of fetal heart rate during labour using Hidden Markov Models," in Proc. IEEE Int. Joint Conf. on Neural Networks, Budapest, Hungary, Jul. 25-29, 2004, vol. 3, pp. 2471-2476.