TY - GEN
T1 - Separating fetal ECG from transabdominal electrical signal
T2 - 6th International Conference on Biological Information and Biomedical Engineering, BIBE 2022
AU - Zhang, Zhao
AU - Wu, Jianhong
AU - Li, Guangfei
AU - Liu, Weifeng
AU - Tang, Xiaoying
N1 - Publisher Copyright:
© VDE VERLAG GMBH.
PY - 2022
Y1 - 2022
N2 - In the clinical fetal monitoring area, it is difficult to separate fetal electrocardiogram (FECG) from the mixed signals of pregnant women due to the influence of electrode displacement, instrument power frequency interference, maternal electrocardiogram (MECG), and electromyogram (EMG). The MECG overlaps greatly with FECG in the frequency domain, while in the time domain, the QRS groups of MECG and FECG are often aliased, which increases the difficulty of FECG extraction. Through the experiment and improvement of the UNet model, we combine deep learning theory and the template subtraction method to separate FECG signals. A cascade model extraction method based on the AE-UNet3+ model is proposed to successfully isolate FECG components, and the effect of the model is evaluated using simulated and real abdominal electric data. In order to extract FECG from mixed signals, the cascade AE-UNet3+ model is used to suppress MECG to obtain residual error, and then the cascade AE-Unet3+ model is used to separate FECG from residual error. The F1 index of R peak detection accuracy on simulated abdominal electrical data and open real data is 97.24% and 94.74%, respectively. Compared with traditional methods (TS, TS-PCA, EKF, and UKF), we verify the effect of our AE-UNet3+ model by comparing the accuracy of R peak detection, signal-to-noise ratio, and fetal heart rate calculation as evaluation indexes, and objectively evaluating the final output prediction FECG.
AB - In the clinical fetal monitoring area, it is difficult to separate fetal electrocardiogram (FECG) from the mixed signals of pregnant women due to the influence of electrode displacement, instrument power frequency interference, maternal electrocardiogram (MECG), and electromyogram (EMG). The MECG overlaps greatly with FECG in the frequency domain, while in the time domain, the QRS groups of MECG and FECG are often aliased, which increases the difficulty of FECG extraction. Through the experiment and improvement of the UNet model, we combine deep learning theory and the template subtraction method to separate FECG signals. A cascade model extraction method based on the AE-UNet3+ model is proposed to successfully isolate FECG components, and the effect of the model is evaluated using simulated and real abdominal electric data. In order to extract FECG from mixed signals, the cascade AE-UNet3+ model is used to suppress MECG to obtain residual error, and then the cascade AE-Unet3+ model is used to separate FECG from residual error. The F1 index of R peak detection accuracy on simulated abdominal electrical data and open real data is 97.24% and 94.74%, respectively. Compared with traditional methods (TS, TS-PCA, EKF, and UKF), we verify the effect of our AE-UNet3+ model by comparing the accuracy of R peak detection, signal-to-noise ratio, and fetal heart rate calculation as evaluation indexes, and objectively evaluating the final output prediction FECG.
UR - http://www.scopus.com/inward/record.url?scp=85145662198&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85145662198
T3 - BIBE 2022 - 6th International Conference on Biological Information and Biomedical Engineering
SP - 150
EP - 154
BT - BIBE 2022 - 6th International Conference on Biological Information and Biomedical Engineering
A2 - Chen, Bin
PB - VDE VERLAG GMBH
Y2 - 19 July 2022 through 20 July 2022
ER -