A clustering-based method for single-channel fetal heart rate monitoring

Abstract

Non-invasive fetal electrocardiography (ECG) is based on the acquisition of signals from abdominal surface electrodes. The composite abdominal signal consists of the maternal electrocardiogram along with the fetal electrocardiogram and other electrical interferences. These recordings allow for the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper introduces a procedure for fetal heart rate extraction from a single-channel abdominal ECG signal. The procedure is composed of three main stages: a method based on wavelet for signal denoising, a new clustering-based methodology for detecting fetal QRS complexes, and a final stage to correct false positives and false negatives. The novelty of the procedure thus relies on using clustering techniques to classify singularities from the abdominal ECG into three types: maternal QRS complexes, fetal QRS complexes, and noise. The amplitude and time distance of all the local maxima followed by a local minimum were selected as features for the clustering classification. A wide set of real abdominal ECG recordings from two different databases, providing a large range of different characteristics, was used to illustrate the efficiency of the proposed method. The accuracy achieved shows that the proposed technique exhibits a competitve performance when compared to other recent works in the literature and a better performance over threshold-based techniques.

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

“…Apart from numerous muscles interferences 16,17 , the strongest one is the maternal electrocardiogram (MECG) of a relatively large amplitude [18][19][20] . Owing to incomplete MECG suppression [21][22][23][24] , the FECG signal becomes unsuitable for further analysis, thus preventing the detection of FQRS complexes, precise location of R waves and FHR determination [25][26][27] .…”

Wearable System for Biosignal Acquisition and Monitoring Based on Reconfigurable Technologies

“…In the case of HR determination, QRS complexes must be located first. For this purpose, two methods have been used: detection based on thresholds, and a method based on the clustering classification of R peaks [39].…”

“…On the other hand, clustering detection [39] is based on the classification of so-called candidate R peaks. These candidate points are local maxima followed by a minimum, which are classified through clustering, using amplitude or time×amplitude as the metric.…”

Cost-Effective Printed Electrodes Based on Emerging Materials Applied to Biosignal Acquisition

“…For the ECG, QRS-complexes detected with the algorithm described above are compared to the annotations made by a cardiologist. The number of false positives (FP), false negatives (FN) and correctly or truly detected (TD) in the determination of QRS-complexes are used to compute the accuracy, A cc , defined as [39]:…”