Multi-channel Non-invasive Fetal Electrocardiography Detection using Wavelet Decomposition

摘要

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.