A clustering-based method for single-channel fetal heart rate monitoring
MetadataShow full item record
AuthorCastillo, Encarnación; Morales, Diego P.; García Ríos, Antonio; Parrilla Roure, Luis; Ruiz, Víctor U.; Álvarez-Bermejo, José A.
Public Library of Science (PLOS)
Castillo E, Morales DP, GarcõÂa A, Parrilla L, Ruiz VU,AÂ lvarez-Bermejo JA (2018) A clustering-based method for single-channel fetal heart rate monitoring. PLoS ONE 13(6): e0199308. [https://doi.org/10.1371/journal.pone.0199308]
SponsorshipThis work has been partially funded by Banco Santander and Centro Mixto UGR-MADOC through project SIMMA (code 2/16). The contribution of Antonio García has been partially funded by Spain's Ministerio de Educación, Cultura y Deporte (Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, within Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016) under a "Salvador de Madariaga" grant (PRX17/00287).
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.