Extraction Algorithm for Morphologically Preserved Non-Invasive Multi-Channel Fetal ECG

Giulia Baldazzi, Danilo Pani, Hau Tieng Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Non-invasive fetal ECG (fECG) is a promising technique that could allow low-cost and risk-free diagnosis, and long-term monitoring of fetal cardiac wellbeing. However, the low quality of the fECG extracted from non-invasive abdominal recordings hampers its adoption in clinical practice. In this work, a new algorithm for the recovery of clean and morphologically preserved fECG signals from multi-channel trans-abdominal recordings is presented. The proposed method exploits optimal shrinkage and nonlocal median algorithms, along with a de-shape short-time Fourier transform-based detection, to recover high-quality fECG traces from a morphological perspective, while ensuring very high performance also in terms of fetal QRS detection. On a small dataset, composed of three real 20 min-long four-channel abdominal ECG recordings, a preliminary performance assessment of the proposed fECG extraction method in terms of fetal QRS detection capabilities revealed a median accuracy of 95.8% and F1 score of 97.9%. The obtained results suggest the possibility of successfully applying this approach for an effective non-invasive fECG extraction, deserving further investigations on larger real and synthetic datasets.

Original languageEnglish (US)
Title of host publication2022 Computing in Cardiology, CinC 2022
PublisherIEEE Computer Society
ISBN (Electronic)9798350300970
StatePublished - 2022
Event2022 Computing in Cardiology, CinC 2022 - Tampere, Finland
Duration: Sep 4 2022Sep 7 2022

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Conference2022 Computing in Cardiology, CinC 2022

ASJC Scopus subject areas

  • General Computer Science
  • Cardiology and Cardiovascular Medicine


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