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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Maternal-Fetal Medicine

Background:

  • Numerous studies focus on non-invasive foetal electrocardiogram (NI-FECG) extraction, primarily for foetal QRS (FQRS) complex detection.
  • Existing research faces challenges due to a lack of public databases, varied performance metrics, and absence of reference algorithms, hindering meaningful assessment.
  • Extraction of NI-FECG morphological features remains underexplored.

Purpose of the Study:

  • To establish a standardized methodology for stress-testing NI-FECG extraction algorithms.
  • To create a comprehensive, realistic artificial dataset for algorithm evaluation.
  • To benchmark the performance of different NI-FECG extraction techniques, including FQRS detection and morphological analysis.

Main Methods:

  • Development of a large artificial NI-FECG signal database (145.8 hours) with non-stationary events.
  • Evaluation of three classes of algorithms: blind source separation (BSS), template subtraction (TS), and adaptive methods (AM).
  • Benchmarking eight NI-FECG algorithms on the artificial database, assessing FQRS detection and morphological parameters (foetal QT, T/QRS ratio).

Main Results:

  • The best performing BSS, AM, and TS methods achieved median FQRS detection accuracies of 99.9%, 97.9%, and 96.0%, respectively.
  • FQRS detection and morphological parameter accuracy are significantly influenced by the extraction technique and signal-to-noise ratio.
  • Evaluation in the source domain after BSS is shown to be unreliable.

Conclusions:

  • The proposed standardized methodology and open-source toolbox (fecgsyn) provide a robust framework for NI-FECG algorithm benchmarking and regulatory testing.
  • Blind source separation (BSS) demonstrates superior performance for FQRS detection compared to adaptive methods and template subtraction.
  • Careful consideration of extraction techniques and signal quality is crucial for accurate NI-FECG analysis, especially for morphological features.