A standardized methodology and artificial database for stress-testing NI-FECG extraction algorithms showed median FQRS detection accuracies of 99.9% for BSS, 97.9% for AM, and 96.0% for TS.
This study provides a standardized, open-source framework and artificial dataset for benchmarking non-invasive foetal ECG extraction algorithms, demonstrating that blind source separation methods achieve the highest FQRS detection accuracy.
Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (FQRS) complexes in terms of location. A small subset of authors have investigated the extraction of morphological features from the NI-FECG. However, due to the shortage of available public databases, the large variety of performance measures employed and the lack of open-source reference algorithms, most contributions cannot be meaningfully assessed. This article attempts to address these issues by presenting a standardised methodology for stress testing NI-FECG algorithms, including absolute data, as well as extraction and evaluation routines. To that end, a large database of realistic artificial signals was created, totaling 145.8 h of multichannel data and over one million FQRS complexes. An important characteristic of this dataset is the inclusion of several non-stationary events (e.g. foetal movements, uterine contractions and heart rate fluctuations) that are critical for evaluating extraction routines. To demonstrate our testing methodology, three classes of NI-FECG extraction algorithms were evaluated: blind source separation (BSS), template subtraction (TS) and adaptive methods (AM). Experiments were conducted to benchmark the performance of eight NI-FECG extraction algorithms on the artificial database focusing on: FQRS detection and morphological analysis (foetal QT and T/QRS ratio). The overall median FQRS detection accuracies (i.e. considering all non-stationary events) for the best performing methods in each group were 99.9% for BSS, 97.9% for AM and 96.0% for TS. Both FQRS detections and morphological parameters were shown to heavily depend on the extraction techniques and signal-to-noise ratio. Particularly, it is shown that their evaluation in the source domain, obtained after using a BSS technique, should be avoided. Data, extraction algorithms and evaluation routines were released as part of the fecgsyn toolbox on Physionet under an GNU GPL open-source license. This contribution provides a standard framework for benchmarking and regulatory testing of NI-FECG extraction algorithms.
Andreotti et al. (Tue,) conducted a other in Non-invasive foetal electrocardiogram (NI-FECG) extraction. NI-FECG extraction algorithms (BSS, TS, AM) was evaluated on FQRS detection accuracy and morphological analysis. A standardized methodology and artificial database for stress-testing NI-FECG extraction algorithms showed median FQRS detection accuracies of 99.9% for BSS, 97.9% for AM, and 96.0% for TS.