Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses

The relevance of the complexity of fetal heart rate fluctuations with regard to the classification of fetal behavioural states has not been satisfyingly clarified so far. Because of the short behavioural states, the permutation entropy provides an advantageous complexity estimation leading to the Kullback–Leibler entropy (KLE). We test the hypothesis that parameters derived from KLE can improve the classification of fetal behaviour states based on classical heart rate fluctuation parameters (SDNN, RMSSD, ln(LF), ln(HF)). From measured heartbeat sequences (35 healthy fetuses at a gestational age between 35 and 40 completed weeks) representative intervals of 256 heartbeats were visually preclassified into fetal behavioural states. Employing discriminant analysis to separate the states 1F, 2F and 4F, the best classification result by classical parameters was 80.0% (SDNN). After additionally considering KLE parameters it was improved significantly (p<0.0005) to 94.3% (ln(LF), KLE_Mean). It could be confirmed that KLE can improve the state classification. This might reflect the consideration of different physiological aspects by classical and complexity measures.

[1]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[2]  Giuseppe Baselli,et al.  Measuring regularity by means of a corrected conditional entropy in sympathetic outflow , 1998, Biological Cybernetics.

[3]  S. Emery,et al.  Dynamic analysis of beat-to-beat fetal heart rate variability recorded by SQUID magnetometer: quantification of sympatho-vagal balance. , 2002, Early human development.

[4]  Dirk Hoyer,et al.  Nonlinear Analysis of the Cardiorespiratory Coordination in a Newborn Piglet , 1998 .

[5]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[6]  H. Bettermann,et al.  Irregularities and nonlinearities in fetal heart period time series in the course of pregnancy , 2000, Herzschrittmachertherapie und Elektrophysiologie.

[7]  S N Erné,et al.  Multi‐channel magnetocardiography for detecting beat morphology variations in fetal arrhythmias , 2004, Prenatal diagnosis.

[8]  C. Martin,et al.  Fetal heart rate variability and behavioral state: analysis by power spectrum. , 1992, American journal of obstetrics and gynecology.

[9]  L. Groome,et al.  Behavioral state organization in normal human term fetuses: the relationship between periods of undefined state and other characteristics of state control. , 1995, Sleep.

[10]  H. Prechtl,et al.  Are there behavioural states in the human fetus? , 1982, Early human development.

[11]  B Arabin,et al.  An attempt to quantify characteristics of behavioral states. , 1992, American journal of perinatology.

[12]  S. Lange,et al.  Multicentre study of fetal cardiac time intervals using magnetocardiography , 2002, BJOG : an international journal of obstetrics and gynaecology.

[13]  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 .

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

[15]  D Grönemeyer,et al.  Fetal heart rate variability and complexity in the course of pregnancy. , 1999, Early human development.

[16]  D. Grönemeyer,et al.  Is there evidence of fetal-maternal heart rate synchronization? , 2003, BMC Physiology.

[17]  R T Wakai,et al.  Spatiotemporal properties of the fetal magnetocardiogram. , 1994, American journal of obstetrics and gynecology.

[18]  S Akselrod,et al.  An Estimate of Fetal Autonomic State by Spectral Analysis of Fetal Heart Rate Fluctuations , 1993, Pediatric Research.

[19]  H W Jongsma,et al.  Classification of fetal and neonatal heart rate patterns in relation to behavioural states. , 1986, European journal of obstetrics, gynecology, and reproductive biology.

[20]  Steven M. Pincus,et al.  A regularity statistic for medical data analysis , 1991, Journal of Clinical Monitoring.

[21]  A Kandori,et al.  Magnetocardiographic determination of the developmental changes in PQ, QRS and QT intervals in the foetus , 2000, Acta paediatrica.

[22]  Solomon Kullback,et al.  Information Theory and Statistics , 1970, The Mathematical Gazette.

[23]  S Cerutti,et al.  Power spectral analysis of the heart rate of the human fetus at 26 and 36 weeks of gestation. , 1989, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[24]  V. Clark,et al.  Computer-aided multivariate analysis , 1991 .

[25]  J. Haueisen,et al.  Signal Analysis of Auditory Evoked Cortical Fields in Fetal Magnetoencephalography , 2004, Brain Topography.

[26]  Dirk Hoyer,et al.  Prognostic impact of autonomic information flow in multiple organ dysfunction syndrome patients. , 2006, International journal of cardiology.

[27]  G G Haddad,et al.  Heart rate control in normal and aborted-SIDS infants. , 1993, The American journal of physiology.

[28]  William H. Press,et al.  Numerical recipes in C , 2002 .

[29]  D. James,et al.  The development of fetal heart rate patterns during normal pregnancy. , 1990, Obstetrics and gynecology.

[30]  Ki H. Chon,et al.  Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales , 2005, IEEE Transactions on Biomedical Engineering.

[31]  J. Kurths,et al.  The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. , 1996, Cardiovascular research.

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

[33]  H. Prechtl,et al.  The behavioural states of the newborn infant (a review). , 1974, Brain research.

[34]  M. Peters,et al.  Monitoring the fetal heart non-invasively: a review of methods , 2001, Journal of perinatal medicine.

[35]  Ronald T. Wakai,et al.  Assessment of fetal neurodevelopment via fetal magnetocardiography , 2004, Experimental Neurology.

[36]  T R Johnson,et al.  Fetal neurobehavioral development. , 1996, Child development.

[37]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  R T Wakai,et al.  Spectral analysis of antepartum fetal heart rate variability from fetal magnetocardiogram recordings. , 1993, Early human development.

[39]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[40]  M. Huotilainen,et al.  Recommended Standards for Fetal Magnetocardiography , 2003, Pacing and clinical electrophysiology : PACE.