Novel Hybrid Extraction Systems for Fetal Heart Rate Variability Monitoring Based on Non-Invasive Fetal Electrocardiogram

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. INDEX TERMS Noninvasive fetal electrocardiography, independent component analysis (ICA), adaptive neuro fuzzy inference system (ANFIS), recursive least squares (RLS), wavelet transform (WT), ICA-ANFIS-WT, ICA-RLS-WT, hybrid methods, fetal heart rate variability monitoring, extraction systems.

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

“…This is caused by maternal movements or cyclical palpation to assess the progress of labour. On the other hand, it is during labour that the measurement environment is the most adverse when it comes to the quality of the FECG indirectly recorded from the abdominal wall 45 . The evaluation of how effective the detection of FQRS complexes is in these signals and in such conditions provides the most reliable information on the usefulness of abdominal electrocardiography.…”

“…In general, all methods for the MECG signal suppression can be divided into three main groups which deploy adaptive filtering, spatial filtering and techniques for subtracting the MECG pattern, as applicable. Different combinations of these methods are also commonly used 19,22,45,48,[67][68][69][70] . The selection of a given MECG suppression method depends on the electrode configuration.…”

A Comparison Between Novel FPGA-Based Pad Monitoring System Using Ballistocardiography and the Conventional Systems for Synchronization and Gating of CMRI at 3 Tesla: A Pilot Study

“…4. The reference sensor is used for post-processing of the signal and adaptive noise cancellation [44]- [46]. The sensor also exists in other embodiments, which can be placed locally under the patient's body or close to the arteries, in the form of a measuring spiral or bellows.…”

Non-Invasive Fetal Electrocardiogram Extraction Based on Novel Hybrid Method for Intrapartum ST Segment Analysis

“…A number of NI-fECG extraction methods have been introduced in the past, including principal component analysis (PCA) [23], [24], independent component analysis (ICA) [23], [25], wavelet transform (WT) [26], adaptive neuro-fuzzy inference system (ANFIS) [27] or least mean squares (LMS) and recursive least squares (RLS) algorithms [28]. Recent studies [29]- [32] have shown that hybrid methods (HM), which combine the advantages of nonadaptive and adaptive approach to fECG extraction, achieve greater accuracy in fECG extraction than when using the methods individually. However, most algorithms presented in these studies as well as the previous research of the authors' team presented in [23], [28]- [30], were able to obtain only a small portion of the large information potential of NI-fECG; their main aim was to determine fetal R positions and use them to estimate the fHR.…”