Annotated real and synthetic datasets for non-invasive foetal electrocardiography post-processing benchmarking
Description
Version 2: Corrected annotations Non-invasive foetal electrocardiography (fECG) can be obtained at different gestational ages by means of surface electrodes applied on the maternal abdomen. The signal-to-noise ratio (SNR) of the fECG is usually low, due to the small size of the foetal heart, the foetal-maternal compartment, the maternal physiological interferences and the instrumental noise. Even after powerful fECG extraction algorithms, a post-processing step could be required to improve the SNR of the fECG signal. In order to support the researchers in the field, this work presents an annotated dataset of real and synthetic signals, which was used for the study “Wavelet Denoising as a Post-Processing Enhancement Method for Non-Invasive Foetal Electrocardiography” [1]. Specifically, 21 15 s-long fECG, dual-channel signals obtained by multi-reference adaptive filtering from real electrophysiological recordings were included. The annotation of the foetal R peaks by an expert cardiologist was also provided. Recordings were performed on 17 voluntary pregnant women between the 21st and the 27th week of gestation. The raw recordings were also included for the researchers interested in applying a different fECG extraction algorithm. Moreover, 40 10 s-long synthetic non-invasive fECG were provided, simulating the electrode placement of one of the abdominal leads used for the real dataset. The annotation of the foetal R peaks was also provided, as generated by the FECGSYN tool used for the signals’ creation. Clean fECG signals were also included for the computation of indexes of signal morphology preservation. All the signals are sampled at 2048 Hz. The data provided in this work can be used as a benchmark for fECG post-processing techniques but can also be used as raw signals for researchers interested in foetal QRS detection algorithms and fECG extraction methods. Please note that, because of versioning issues, the v1 version of our dataset included some annotation errors (6%) with respect to the real data effectively used in our published works. Therefore, please download the current version (v2) of the dataset.
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Steps to reproduce
Scientists who already downloaded the dataset in version 1 are kindly encouraged to replace it by downloading version 2.