Improving fetal heart rate signal interpretation by application of myriad filtering

• Autorzy: Wróbel J. , Horoba K. , Pander T. , Jeżewski J. , Czabański R.
• Języki publikacji: EN

Abstrakty

EN

Analysis of the fetal heart rate (FHR) signal is aimed at detection of clinically important patterns like bradycardia or tachycardia, accelerations and decelerations, as well as quantification of instantaneous FHR variability. Automated pattern recognition methods are based on estimation of so-called FHR baseline. It is a common opinion that the baseline estimation algorithm determines the efficiency of an entire process of quantitative signal analysis. Automated methods for baseline determination have been continuously improved for many years since there are still new classes of FHR signals being identified, for which the previous methods fail. The new method proposed for the baseline estimation is based on the weighted myriad filtering. The application of this method required filter parameter selection ensuring its operation according to clinical guidelines for baseline estimation. A very important feature of the myriad filtering is that there is no need for preliminary interpolation of signal loss segments. Our new algorithm was tested against two other methods. Thirty one-hour FHR recordings were selected for the analysis. Quantitative inconsistency was measured using differences between corresponding baseline samples. Additionally, the baselines were evaluated as regards their influence on identification of the acceleration and deceleration patterns. Obtained results allow us to conclude that the new algorithm delivers more reliable baselines particularly for signals with specific changes of the basal FHR level which has been recognized as difficult for baseline estimation.

Słowa kluczowe

EN

electronic fetal monitoring; fetal heart rate analysis; baseline estimation; signal processing

PL

monitorowanie płodu; tętno płodu; przetwarzanie sygnału

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2013
• Tom: Vol. 33, no. 4
• Strony: 211--221
• Opis fizyczny: Bibliogr. 41 poz., tab., wykr.

Twórcy

autor

Wróbel J.

• Department of Biomedical Signal Processing, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800 Zabrze, Poland

autor

Horoba K.

• Department of Biomedical Signal Processing, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800 Zabrze, Poland

autor

Pander T.

• Institute of Electronics, Silesian University of Technology, Gliwice, Poland

autor

Jeżewski J.

• Department of Biomedical Signal Processing, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800 Zabrze, Poland

autor

Czabański R.

• Institute of Electronics, Silesian University of Technology, Gliwice, Poland

Bibliografia

• [1] Aeyels B, van-der-Perre G, Pello L, van-Assche A, Spitz B. On line processing of perinatal fetal heart rate and intra uterine pressure. In: Proceedings of the 14th International Conference of the IEEE EMBS (Paris); 1992. pp. 2728–9.
• [2] Arce G. Nonlinear signal processing: a statistical approach. New York: Wiley; 2005.
• [3] Arduini D, Rizzo G, Giannini F, Garzetti G, Romanini C. Computerized analysis of fetal heart rate: II. Comparison with the interpretation of experts. J Matern Fetal Invest 1993; 3: 165–8.
• [4] Arduini D, Rizzo G, Piana G, Bonalumi A, Brambilla P, Romanini C. Computerized analysis of fetal heart rate: I. Description of the system (2CTG). J Matern Fetal Invest 1993; 3: 159–63.
• [5] Ayres-de-Campos D, Bernardes J. Twenty-five years after the FIGO guidelines for the use of fetal monitoring: time for a simplified approach. Int J Gynaecol Obstet 2010; 110: 1–6.
• [6] Ayres-de-Campos D, Bernardes J, Marsal K, Nickelsen C, Makarainen L, Banfield P, et al. Can the reproducibility of fetal heart rate baseline estimation be improved? Eur J Obstet Gynecol Reprod Biol 2004; 112: 49–54.
• [7] Bernardes J, Moura C. The Porto system for automated cardiotocographic signal analysis. J Perinat Med 1991; 19: 61–5.
• [8] Bernardes J, Costa-Pereira A, van-Geijn H, Pereira-Leite L. A more objective fetal heart rate baseline estimation. Br J Obstet Gynaecol 1996; 103: 714–5.
• [9] Chan S, Zou Y. A Recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis. IEEE Trans Signal Process 2004; 52: 975–91.
• [10] Costa M, Ayres-de-Campos D, Machadao A, Santos C, Bernardes J. Comparison of computer system evaluation of intrapartum cardiotocographic events and a consensus of clinicians. J Perinat Med 2010; 38: 191–5.
• [11] Czabański R, Jeżewski J, Matonia A, Jeżewski M. Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia. Expert Syst Appl 2012; 39 (15): 11846–60.
• [12] Dalton K, Dawson A. Baseline: a computer method of calculating baseline in fetal heart rate recordings. Int J Biomed Comput 1984; 15: 311–7.
• [13] Dawes G, Houghton C. Baseline in human fetal heart rate records. Br J Obstet Gynaecol 1982; 89: 270–5.
• [14] Dawes G, Moulden M, Redman C. System 8000: computerized antenatal FHR analysis. J Perinat Med 1991; 19: 47–51.
• [15] Devoe LD, Golde S, Kilman Y, Morton D, Shea K, Waller J. A comparison of visual analyses of intrapartum fetal heart rate tracings according to the new National Institute of Child Health and Human Development guidelines with computer analyses by an automated fetal heart rate monitoring system. Am J Obstet Gynecol 2000; 183: 361–6.
• [16] Georgieva A, Payne S, Moulden M, Redman C. Computerized fetal heart rate analysis in labor: detection of intervals with unassignable baseline. Physiol Meas 2011; 32: 1549–60.
• [17] Gonzalez J, Arce G. Optimality of the myriad filter in practical impulsive-noise environments. IEEE Trans Signal Process 2001; 49: 438–41.
• [18] Gonzalez J, Arce G. Statistically-efficient filtering in impulsive environments: weighted myriad filters EURASIP. J Appl Signal Process 2002; 1: 4–20.
• [19] Gonzalez J, Griffith D, Arce G. Matched myriad filtering for robust communications. In: Proceedings Conference on Information Science & Systems; (New York);1996.
• [20] Hong X, Chen S. M-estimator and D-optimality model construction using orthogonal forward regression. IEEE Trans Syst Man Cybern 2005; 35: 1–7.
• [21] Jezewski J, Wrobel J, Horoba K, Gacek A, Sikora J. Fetal heart rate variability: clinical experts versus computerized system interpretation. In: Proceedings of the 24th International Conference of the IEEE EMBS (Huston); 2002. pp. 1617–8.
• [22] Jezewski J, Wrobel J, Horoba K, Graczyk S, Gacek A, Sikora J. Computerized perinatal database for retrospective qualitative assessment of cardiotocographic traces. In: Proceedings of the International Conference on Current Perspectives in Healthcare Computing (Harrogate); 1996. pp. 187–96.
• [23] Jezewski J, Horoba K, Gacek A, Wrobel J, Matonia A, Kupka T., Analysis of nonstationarities in fetal heart rate signal: inconsistency measures of baselines using acceleration/deceleration patterns. In: IEEE Proceedings of the 7th International Symposium on Signal Processing and its Applications, ISSPA, (Paris); 2003. pp. 34-8.
• [24] Jezewski J, Horoba K, Matonia A. Baseline and acceleration episodes – clinically significant nonstationarities in FHR signal: II. Indirect comparison. In: Kurzynski M, Puchala E, et al., editors. Computer recognition systems, Advances in Soft Computing, vol. 30. Berlin/Heidelberg: Springer Verlag; 2005. pp. 535–42.
• [25] Jezewski J, Roj D, Wrobel J, Horoba K. A novel technique for fetal heart rate estimation from Doppler ultrasound signal. Biomedical Engineering Online 2011; 10: 92. http://dx.doi.org/ 10.1186/1475-925X-10-92.
• [26] Jezewski J, Wrobel J, Kupka T. Baseline and acceleration episodes – clinically significant nonstationarities in FHR signal: I. Coefficients of inconsistency. In: Kurzynski M, Puchala E, et al., editors. Computer recognition systems, Advances in Soft Computing, vol. 30. Berlin/Heidelberg: Springer Verlag; 2005. pp. 527–34.
• [27] Jezewski M, Czabanski R, Wrobel J, Horoba K. Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome. Biocybern Biomed Eng 2010; 30: 39–47.
• [28] Jimenez L, Gonzalez R, Gaitan M, Carrasco S, Vargas C. Computerized algorithm for baseline estimation of fetal heart rate. Comput Cardiol (Memphis) 2002; 29: 477–80.
• [29] Kalluri S, Arce G. Fast algorithms for weighted myriad computation by fixed point search. IEEE Trans Signal Process 2002; 48: 159–71.
• [30] Kalluri S, Arce G. Robust frequency-selective filtering using weighted myriad filters admitting real-valued weights. IEEE Trans Signal Process 2001; 49: 2721–33.
• [31] Labaj P, Jezewski M, Matonia A, Kupka T, Jezewski J, Gacek A., New approach to quantitative description of deceleration of fetal heart rate for the patterns classification. In: Proceedings of the 29th International Conference of the IEEE/EMBS, vol. VIII; (Lyon); 2007. pp. 3156-9.
• [32] Lim HS, Chuah TC, Chuah HT. On the optimal alpha-k curve of the sample myriad. IEEE Signal Proc Lett 2007; 14: 545–8.
• [33] Maconnes G, Hankins G, Spong C, Moore T. The 2008 National Institute of Child Health and Human Development Workshop Report on Electronic Fetal Monitoring: update on definitions, interpretation, and research guidelines. JOGNN 2008; 37: 510–5.
• [34] Mantel R, van-Geijn H, Caron F, Swartjes J, van-Woerden EE, Jongsma HW. Computer analysis of antepartum fetal heart rate: 1. Baseline determination. Int J Biomed Comput 1990; 25: 261–72.
• [35] Marques-de-Sa J, Reis L, Lau J, Bernardes J. Estimation and classification of fetal heart rate baselines using artificial neural networks. Comput Cardiol (Bethesda) 1994; 21: 541–4.
• [36] Nidhal S, Mohd-Ali M, Zaidan A, Zaidan B, Najah H. Computerized algorithm for fetal heart rate baseline and baseline variability estimation based on distance between signal average and alpha value. Int J Pharmacol 2011; 7: 228–37.
• [37] Nunez R, Gonzalez J, Arce G. Fast and accurate computation of the myriad filter via branch-and-bound search. IEEE Trans Signal Process 2011; 56: 3340–6.
• [38] Pander T. The class of M-filters in the application of ECG signal processing. Biocybern Biomed Eng 2006; 26: 3–13.
• [39] Pander T. New polynomial approach to myriad filter computation. Signal Process 2010; 90: 1991–2001.
• [40] RCOG. The use of electronic fetal monitoring. Evidence-based clinical guideline. London: Royal College of Obstetricians and Gynaecologists (RCOG Press); 2001.
• [41] Rooth G, Huch A, Huch R. Guidelines for the use of fetal monitoring. Int J Gynaecol Obstet 2007; 25: 159–67.