Assessment of non-linear features for intrapartal fetal heart rate classification

Fetal heart rate (fHR) is used to evaluate the fetal well-being during the delivery. It provides information of fetal status and allows doctors to detect ongoing hypoxia. The routine intrapartal evaluation is based on description of macroscopic morphological features of the fHR baseline. FHR contains more information than is used so far, therefore in this work we have focused on evaluation of nonlinear features for fHR signal description. Our data set consists of 189 recordings. Signals with umbilical artery pH less then 7.15 were considered pathological. From each record, 20-minute segment directly preceding the delivery, was chosen. Artifacts were removed from the data and all segments were resampled to 4 Hz sampling frequency. Fractal dimension of attractor, fractal dimension of waveform, entropy, and complexity were used as features. The particular methods used to compute the features were: correlation method for estimation of attractor dimension; Higuchi's, variance, and box counting method for estimation of waveform fractal dimension; approximate and sample method for estimation of entropy and also the Lempel Ziv complexity was computed. All features were evaluated using Mann-Whitney U test and those fulfilling the statistical significance with p<0.01 were used for further computations. Ten fold cross-validation classification using decision tree and SVM approach was carried out. Overall sensitivity and specificity of 70%, comparable to inter-observer variability, was acquired.

[1]  S. Cerutti,et al.  Complexity analysis of 24 hours heart rate variability time series , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  J. M. Swartjes,et al.  Computer analysis of antepartum fetal heart rate: 2. Detection of accelerations and decelerations. , 1990, International journal of bio-medical computing.

[3]  E. Ifeachor,et al.  A Comparative Study of Fetal Heart Rate Variability Analysis Techniques , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Pincus Sm,et al.  Approximate Entropy: A Regularity Measure for Fetal Heart Rate Analysis , 1992, Obstetrics and gynecology.

[5]  K. Maršál,et al.  Fetal heart rate patterns and ECG ST segment changes preceding metabolic acidaemia at birth , 2005, BJOG : an international journal of obstetrics and gynaecology.

[6]  Tiina Luukkaala,et al.  Intrapartum cardiotocography – the dilemma of interpretational variation , 2006, Journal of Perinatal Medicine.

[7]  Amparo Alonso-Betanzos,et al.  Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system , 2002, Artif. Intell..

[8]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[9]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[10]  Chrysostomos D. Stylios,et al.  Feature Extraction and Classification of Fetal Heart Rate Using Wavelet Analysis and Support Vector Machines , 2006, Int. J. Artif. Intell. Tools.

[11]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[12]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[13]  Steven M. Pincus,et al.  Approximate Entropy: A Regularity Measure for Fetal Heart Rate Analysis , 1992, Obstetrics and gynecology.

[14]  Maria G. Signorini,et al.  Classification of cardiotocographic records by neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[15]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[16]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[17]  Ana Paula Rocha,et al.  Linear and nonlinear fetal heart rate analysis of normal and acidemic fetuses in the minutes preceding delivery , 2006, Medical and Biological Engineering and Computing.

[18]  David Ruelle,et al.  Deterministic chaos: the science and the fiction , 1995 .

[19]  Z. J. Yang,et al.  The prediction of fetal acidosis at birth by computerised analysis of intrapartum cardiotocography , 1995, British journal of obstetrics and gynaecology.

[20]  J. V. van Laar,et al.  Spectral analysis of fetal heart rate variability for fetal surveillance: review of the literature , 2008, Acta obstetricia et gynecologica Scandinavica.

[21]  D. Koutsouris,et al.  Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks , 2002, BJOG : an international journal of obstetrics and gynaecology.

[22]  J. P. Marques de Sá,et al.  The Porto system for automated cardiotocographic signal analysis , 1991, Journal of perinatal medicine.

[23]  George J. Vachtsevanos,et al.  A comparison of fractal dimension algorithms using synthetic and experimental data , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[24]  T. Higuchi.  Approach to an irregular time series on the basis of the fractal theory , 1988 .

[25]  S. Pincus.  Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.