@inproceedings {Singapore,
	title = {An Experimental Study on Transferring Data-Driven Image Compressive Sensing to Bioelectric Signals},
	year = {2022},
	month = {05/2022},
	pages = {1191-1195},
	address = {Singapore},
	abstract = {<p>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 ac- curacy 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.</p>
},
	keywords = {psclab},
	doi = {10.1109/ICASSP43922.2022.9747439},
	author = {Zhikang Zhang and Jonathan Zhao and Fengbo Ren}
}
