Abstract
This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.
Highlights
Before the advent of electronics in obstetrics and gynaecology, doctors had to rely on their senses and experience
Three experiments were carried out using signals from ADFECGDB database and one experiment was carried out using signals from database Physionet Challenge 2013
The determination of true positive values (TP), false positive values (FP) and false negative values (FN) from all fetal electrocardiogram (fECG) signals extracted will be performed on 12 recordings from the ADFECGDB database using both hybrid methods and the ACC, SE, positive predictive value (PPV) and F1 parameters will be calculated to determine extraction accuracy
Summary
Before the advent of electronics in obstetrics and gynaecology, doctors had to rely on their senses and experience. One of the first methods to detect non-invasive fetal cardiac activity was to listen to (auscultation) heart sounds using a stethoscope [1] In this way, only basic information such as indicative fetal heart rate (fHR), significant arrhythmias, or cardiac arrest could be obtained about fetal health [2]. EFM breeds the possibility of continuous monitoring and, early detection of symptoms of fetal hypoxia [5] and other life-threatening conditions. This has led to a significant reduction in neonatal mortality, as shown, for example, by the study presented by Chen et al in [6].
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