TY - GEN
T1 - Multi-modal Framework for Fetal Heart Rate Estimation
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
AU - Shokouhmand, Arash
AU - Antoine, Clarel
AU - Young, Bruce K.
AU - Tavassolian, Negar
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This study presents a novel multi-modal framework for fetal heart rate extraction, which incorporates wearable seismo-cardiography (SCG), gyro-cardiography (GCG), and electrocardiography (ECG) readings from ten pregnant women. Firstly, a signal refinement method based on empirical mode decomposition (EMD) is proposed to extract the desired signal components associated with fetal heart rate (FHR). Afterwards, two techniques are developed to fuse the information from different modalities. The first method, named early fusion, is intended to combine the refined signals of different modalities through intra-modality fusion, intermodality fusion, and FHR estimation. The other fusion approach, i.e., late fusion, includes FHR estimation and intermodality FHR fusion. FHR values are estimated and compared with readings from a simultaneously-recorded cardiotocography (CTG) sensor. It is demonstrated that the best performance belongs to the late-fusion approach with 87.00% of positive percent agreement (PPA), 6.30% of absolute percent error (APE), and 10.55 beats-per-minute (BPM) of root-meansquare-error (RMSE).Clinical Relevance - The proposed framework allows for the continuous monitoring of the health status of the fetus in expectant women. The approach is accurate and cost-effective due to the use of advanced signal processing techniques and lowcost wearable sensors, respectively.
AB - This study presents a novel multi-modal framework for fetal heart rate extraction, which incorporates wearable seismo-cardiography (SCG), gyro-cardiography (GCG), and electrocardiography (ECG) readings from ten pregnant women. Firstly, a signal refinement method based on empirical mode decomposition (EMD) is proposed to extract the desired signal components associated with fetal heart rate (FHR). Afterwards, two techniques are developed to fuse the information from different modalities. The first method, named early fusion, is intended to combine the refined signals of different modalities through intra-modality fusion, intermodality fusion, and FHR estimation. The other fusion approach, i.e., late fusion, includes FHR estimation and intermodality FHR fusion. FHR values are estimated and compared with readings from a simultaneously-recorded cardiotocography (CTG) sensor. It is demonstrated that the best performance belongs to the late-fusion approach with 87.00% of positive percent agreement (PPA), 6.30% of absolute percent error (APE), and 10.55 beats-per-minute (BPM) of root-meansquare-error (RMSE).Clinical Relevance - The proposed framework allows for the continuous monitoring of the health status of the fetus in expectant women. The approach is accurate and cost-effective due to the use of advanced signal processing techniques and lowcost wearable sensors, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85122520145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122520145&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9629975
DO - 10.1109/EMBC46164.2021.9629975
M3 - Conference contribution
C2 - 34892753
AN - SCOPUS:85122520145
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7166
EP - 7169
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -