Fetal electrocardiography (FECG) places electrodes on the maternal abdomen to convert the fetal electrocardiosignals into fetal heart rate (FHR), improving the accuracy and comfort of pregnant woman. At the same time, FECG simplifies the procedure of long term monitoring in the perinatal period.
Investigating the influence of gestational age and time of day on FHR features to distinguish between non-stress test (NST) normal fetuses and NST suspicious fetuses.
A novel method of FHR baseline estimation was presented; then baseline value and fetal heart rate variation (FHRV) were analyzed in the time domain using FHR signals recorded from 52 fetuses.
Baseline values in 1:00, 2:00, 4:00, 5:00 and heart rate variation (HRV) distribution showed a significant difference (p< 0.05) between NST normal fetuses and NST suspicious fetuses.
The results suggest that NST normal and suspicious fetuses had same outcome and different FHR features. Accurately distinguishing normal fetuses and suspicious fetuses is important for lowering the false positive rate and reducing unnecessary clinical intervention.
Serra V, , Bellver J, , Moulden M, and Redman CWG. Computerized analysis of normal fetal heart rate pattern throughout gestation. Ultrasound in Obstetrics & Gynecol. 2009: 34(1): 74-79.
Kupka T, , Wrobel J, , Jezewski J, , Gacek A. Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model. EMBS Annual International Conference. New York City, USA. 2006: 28: 3728-3731.
Dawes GS, , Moulden M, , Redman CWG. Improvements in computerized fetal heart rate analysis antepartum. J Perinat Med. 1996: 24(1): 25-36.
Jezewski J, and Wrobel J. Global baseline determination in fetal heart rate records with the identification of local prominent rates. Medical Physics and Biomedical Engineering. 1994: 2: 365-369.
Jezewski J, , Horoba K, , Gacek A, , Wrobel J, , Matonia A, and Kupka T. Analysis of nonstationarities in fetal heart rate signal: Inconsistency measures of baselines using acceleration/deceleration patterns. Signal Processing and its Applications. 2003: 2(2): 9-12.
Magenes G, , Signorini MG, and Arduini D. Classification of cardiotocographic records by neural networks Neural Networks. IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks. 2000: 3: 637-641.
Pillai M, and James D. The Development of Fetal Heart Rate Patterns During Normal Pregnancy. Obstetrics & Gynecology. 1990: 76(5): 812-816.
Lange DS, , Leeuwen PV, , Geue D et al. Influence of gestational age, heart rate, gender and time of day on fetal heart rate variability, Medical & Biological Engineering & Computing. 2005: 43(4): 481-486.
Brandle J, , Preissl H, and Draganova R. Heart rate variability parameters and fetal movement complement fetal behavioral states detection via magnetography. Frontiers in Human Neuroscience. 2015: 9: 1-8.
Lucchini M, , Signorini MG, and William PF. Multi-parametric Heart Rate Analysis in Premature Babies exposed to Sudden Infant Death Syndrome. Conf Proc IEEE Eng Med Biol Soc. 2014: 6389-6392.
Xu L, , Redman CWG, and Payne SJ. Feature selection using genetic aigorithms for fetal heart rate analysis. Physiological Measurement. 2014: 35(7): 1357-1371.
Alickovic E, and Subasi A. Effect of Multiscale PCA De-noising in ECG beat classification for diagnosis of cardiovascular diseases. Circuits Systems & Signal Processing. 2015: 34(2): 513-533.