An Adaptive Filtering Technique for Enhancing Extraction of Foetal Electrocardiographic Signal from Abdominal Electrocardiogram

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@article{1716023,
      author = {Orisakwe Chinonso Ndunaka, Mbachu C. B., Nzeife I. D., Muoghalu C. N.},
      title = {An Adaptive Filtering Technique for Enhancing Extraction of Foetal Electrocardiographic Signal from Abdominal Electrocardiogram},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {566-573},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716023.pdf},
      abstract = {non-invasive foetal electrocardiogram (FECG) monitoring provides vital clinical information for assessing foetal well-being during pregnancy. However, abdominally recorded ECG signals are heavily contaminated by maternal ECG (MECG) and noise, making accurate FECG extraction challenging. This study proposes a Blackman-windowed finite impulse response (FIR) adaptive filtering approach for improved separation of FECG from composite abdominal ECG (AECG) signals. Unlike conventional adaptive FIR filters, the proposed method applies final coefficient windowing to enhance stability, reduce distortion, and improve signal-to-noise ratio (SNR). The system is implemented and evaluated through MATLAB simulations. Performance is assessed using SNR and mean square error (MSE) and compared with conventional LMS-based adaptive filters. Results demonstrate that the proposed Blackman-windowed adaptive filter provides superior FECG extraction quality, validating its suitability for non-invasive foetal monitoring applications.},
      keywords = {Foetal ECG, Adaptive Filtering, Blackman Window, LMS Algorithm, Biomedical Signal Processing},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716023}
  }