TY - GEN
T1 - Swarm Decomposition of Abdominal Signals for Non-invasive Fetal ECG Extraction
AU - Abuhantash, Ferial
AU - Khandoker, Ahsan H.
AU - Apostolidis, Georgios K.
AU - Hadjileontiadis, Leontios J.
N1 - Funding Information:
The study was partly supported by an internal grant awarded to Ahsan H. Khandoker, PhD (CIRA 2019-023 grant Project 8474000174).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The non-invasive fetal electrocardiography (fECG) extraction from maternal abdominal signals is one of the most promising modern fetal monitoring techniques. However, the noninvasive fECG signal is heavily contaminated with noise and overlaps with other prominent signals like the maternal ECG. In this work we propose a novel approach in non-invasive fECG extraction using the swarm decomposition (SWD) to isolate the fetal components from the abdominal signal. Accompanied with the use of higher-order statistics (HOS) for R peak detection, the application of the proposed method to the Abdominal and Direct Fetal ECG PhysioNet Database resulted in fetal R peak detection sensitivity of 99.8% and a positive predictability of 99.8%. Our results demonstrate the applicability of SWD and its potentiality in extracting fECG of good morphological quality with more deep decomposition levels, in order to connect the extracted structural characteristics of the fECG with the health status of the fetus.Clinical Relevance - The developed method shows improvement in fetal R peak detection for certain signals.
AB - The non-invasive fetal electrocardiography (fECG) extraction from maternal abdominal signals is one of the most promising modern fetal monitoring techniques. However, the noninvasive fECG signal is heavily contaminated with noise and overlaps with other prominent signals like the maternal ECG. In this work we propose a novel approach in non-invasive fECG extraction using the swarm decomposition (SWD) to isolate the fetal components from the abdominal signal. Accompanied with the use of higher-order statistics (HOS) for R peak detection, the application of the proposed method to the Abdominal and Direct Fetal ECG PhysioNet Database resulted in fetal R peak detection sensitivity of 99.8% and a positive predictability of 99.8%. Our results demonstrate the applicability of SWD and its potentiality in extracting fECG of good morphological quality with more deep decomposition levels, in order to connect the extracted structural characteristics of the fECG with the health status of the fetus.Clinical Relevance - The developed method shows improvement in fetal R peak detection for certain signals.
UR - http://www.scopus.com/inward/record.url?scp=85122496834&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9631017
DO - 10.1109/EMBC46164.2021.9631017
M3 - Conference contribution
C2 - 34891405
AN - SCOPUS:85122496834
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 775
EP - 778
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -