17 November 2017 Multi-channel non-invasive fetal electrocardiography detection using wavelet decomposition
Author Affiliations +
Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 105720U (2017) https://doi.org/10.1117/12.2286749
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia
Abstract
Non-invasive fetal electrocardiography (fECG) has attracted the medical community because of the importance of fetal monitoring. However, its implementation in clinical practice is challenging: the fetal signal has a low Signal- to-Noise-Ratio and several signal sources are present in the maternal abdominal electrocardiography (AECG). This paper presents a novel method to detect the fetal signal from a multi-channel maternal AECG. The method begins by applying filters and signal detrending the AECG signals. Afterwards, the maternal QRS complexes are identified and subtracted. The residual signals are used to detect the fetal QRS complex. Intervals of these signals are analyzed by using a wavelet decomposition. The resulting representation feds a previously trained Random Forest (RF) classifier that identifies signal intervals associated to fetal QRS complex. The method was evaluated on a public available dataset: the Physionet2013 challenge. A set of 50 maternal AECG records were used to train the RF classifier. The evaluation was carried out in signals intervals extracted from additional 25 maternal AECG. The proposed method yielded an 83:77% accuracy in the fetal QRS complex classification task.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Javier Almeida , Javier Almeida , Josué Ruano, Josué Ruano, Germán Corredor, Germán Corredor, David Romo-Bucheli, David Romo-Bucheli, José Ricardo Navarro-Vargas, José Ricardo Navarro-Vargas, Eduardo Romero, Eduardo Romero, } "Multi-channel non-invasive fetal electrocardiography detection using wavelet decomposition", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105720U (17 November 2017); doi: 10.1117/12.2286749; https://doi.org/10.1117/12.2286749
PROCEEDINGS
8 PAGES


SHARE
Back to Top