Robust fetal QRS detection from noninvasive abdominal electrocardiogram based on channel selection and simultaneous multichannel processing

Abstract

The purpose of this study is to provide a new method for detecting fetal QRS complexes from non-invasive fetal electrocardiogram (fECG) signal. Despite most of the current fECG processing methods which are based on separation of fECG from maternal ECG (mECG), in this study, fetal heart rate (FHR) can be extracted with high accuracy without separation of fECG from mECG. Furthermore, in this new approach thoracic channels are not necessary. These two aspects have reduced the required computational operations. Consequently, the proposed approach can be efficiently applied to different real-time healthcare and medical devices. In this work, a new method is presented for selecting the best channel which carries strongest fECG. Each channel is scored based on two criteria of noise distribution and good fetal heartbeat visibility. Another important aspect of this study is the simultaneous and combinatorial use of available fECG channels via the priority given by their scores. A combination of geometric features and wavelet-based techniques was adopted to extract FHR. Based on fetal geometric features, fECG signals were divided into three categories, and different strategies were employed to analyze each category. The method was validated using three datasets including Noninvasive fetal ECG database, DaISy and PhysioNet/Computing in Cardiology Challenge 2013. Finally, the obtained results were compared with other studies. The adopted strategies such as multi-resolution analysis, not separating fECG and mECG, intelligent channels scoring and using them simultaneously are the factors that caused the promising performance of the method.

Efficient Fetal-Maternal ECG Signal Separation from Two Channel Maternal Abdominal ECG via Diffusion-Based Channel Selection

“…First, based on the geometry of the inherited oscillatory structure of the cardiac activity, the diffusion-based manifold learning technique is applied to do the channel section. While other channel selection criteria mainly are based on the power spectral distribution, wave morphology entropy, root mean square error, etc, to find the clearest and most enhanced QRS complexes [15,22], our approach is different since we carefully examine the nontrivial underlying geometric structure hosting the cardiac activity by the DM and look for the linear combination that is most like a simple closed curve. Second, we apply the modern time-frequency analysis technique, the dsSTFT, and the beat tracking algorithms detailed in [50] to obtain an accurate R peak locations, and the nonlocal median, to better estimate the maternal ECG morphology and fetal ECG morphology.…”

Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage

“…There have been many attempts to conquer this challenge in the past decades, and we can roughly classify those attempts into three categories -(a) multiple ta-mECG channels, with or without one or more maternal thoracic ECG signals; (b) only single ta-mECG channel; (c) few (two or three) ta-mECG channels. Most attempts fall in category (a), and researchers apply algorithms like blind source separation (BSS) [18,19,20,21], semi-BSS like periodic component analysis [22,23,24], adaptive filtering like approaches [25,26,27,28,29,30,31], and others [32,33,34]. When there is only a single channel ta-mECG in category (b), researchers consider approaches like template subtraction (TS) [35,36,37,38,39,40,41,42], time-frequency analysis [43,44,45,46,47,48], sequential total variation [49], or state space reconstruction via lag map [50,51,52].…”

Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation

“…For the extraction of FECG, many sophisticated signal processing methods have been used in literature. The approaches based on the adaptive filtering which are combined with wavelet transform [2], singular value decomposition [3], cross-correlation method and orthogonal basis [4], independent component analysis and wavelet-based techniques [5,6] and so forth. These approaches can be categorized based on the principal signal processing methodologies which are adaptive filtering, linear decomposition and non-linear decomposition.…”