TY - GEN
T1 - Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation
AU - Shokouhmand, Arash
AU - Tavassolian, Negar
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals. The signals are initially segmented into 4-second segments and then fed into the network for denoising. It is demonstrated that the signal-to-signal translation method can reconstruct clean AECG signals with average mean-absolute-error (MAE), root-mean-square deviation (RMSD), and Pearson correlation coefficient (PCC) of 0.099, 0.124, and 99.12% respectively, between clean and denoised AECG signals. Furthermore, the robustness of the method to low signal-to-noise ratio (SNR) input values is shown by an RMSD range of (0.047, 0.352) for SNR values within the range of (-3, 3) dB. Clinical Relevance- The proposed framework allows for the denoising of abdominal ECG signals for non-invasive fetal heart rate monitoring.
AB - This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals. The signals are initially segmented into 4-second segments and then fed into the network for denoising. It is demonstrated that the signal-to-signal translation method can reconstruct clean AECG signals with average mean-absolute-error (MAE), root-mean-square deviation (RMSD), and Pearson correlation coefficient (PCC) of 0.099, 0.124, and 99.12% respectively, between clean and denoised AECG signals. Furthermore, the robustness of the method to low signal-to-noise ratio (SNR) input values is shown by an RMSD range of (0.047, 0.352) for SNR values within the range of (-3, 3) dB. Clinical Relevance- The proposed framework allows for the denoising of abdominal ECG signals for non-invasive fetal heart rate monitoring.
UR - http://www.scopus.com/inward/record.url?scp=85138126746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138126746&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871826
DO - 10.1109/EMBC48229.2022.9871826
M3 - Conference contribution
C2 - 36086419
AN - SCOPUS:85138126746
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2017
EP - 2020
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -