TY - GEN
T1 - AN EXPERIMENTAL STUDY ON TRANSFERRING DATA-DRIVEN IMAGE COMPRESSIVE SENSING TO BIOELECTRIC SIGNALS
AU - Zhang, Zhikang
AU - Zhao, Jonathan
AU - Ren, Fengbo
N1 - Funding Information:
This work is supported by a NSF grant (IIS/CPS-1652038) and an unrestricted gift (CG#1490376) from Cisco Research Center. The NVIDIA GPUs used for this research were donated by the NVIDIA Corporation.
Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - The emerging area of bioelectric signal compressive sensing(CS) has shown great potential in health care applications. However, improving the reconstruction accuracy of compressively sensed bioelectric signals remains a challenging problem. In recent years, data-driven image CS methods have achieved significant improvements in reconstruction accuracy over conventional model-based image CS methods. In this paper, we conduct an experimental study on transferring existing data-driven image CS methods to bioelectric signals. Through our investigation of five critical factors affecting the reconstruction performance of bioelectric signals, we conclude that existing data-driven image CS methods can be transferred to ECG signals with high reconstruction accuracy. Our experimental results show that transferred data-driven image CS methods can achieve up to 8.08-2.73 SNR improvement over the reference method on ECG signal reconstruction across compression ratios of 2-8x.
AB - The emerging area of bioelectric signal compressive sensing(CS) has shown great potential in health care applications. However, improving the reconstruction accuracy of compressively sensed bioelectric signals remains a challenging problem. In recent years, data-driven image CS methods have achieved significant improvements in reconstruction accuracy over conventional model-based image CS methods. In this paper, we conduct an experimental study on transferring existing data-driven image CS methods to bioelectric signals. Through our investigation of five critical factors affecting the reconstruction performance of bioelectric signals, we conclude that existing data-driven image CS methods can be transferred to ECG signals with high reconstruction accuracy. Our experimental results show that transferred data-driven image CS methods can achieve up to 8.08-2.73 SNR improvement over the reference method on ECG signal reconstruction across compression ratios of 2-8x.
KW - bioelectric signal
KW - compressive sensing
KW - deep learning
UR - http://www.scopus.com/inward/record.url?scp=85131253350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131253350&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9747439
DO - 10.1109/ICASSP43922.2022.9747439
M3 - Conference contribution
AN - SCOPUS:85131253350
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1191
EP - 1195
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -