TY - CHAP
T1 - SoC-based architecture for biomedical signal processing
AU - Gutiérrez-Rivas, R.
AU - Hernández, A.
AU - García, J. J.
AU - Marnane, W.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Over the last decades, many algorithms have been proposed for processing biomedical signals. Most of these algorithms have been focused on the elimination of noise and artifacts existing in these signals, so they can be used for automatic monitoring and/or diagnosis applications. With regard to remote monitoring, the use of portable devices often requires a reduced number of resources and power consumption, being necessary to reach a trade-off between the accuracy of algorithms and their computational complexity. This paper presents a SoC (System-on-Chip) architecture, based on a FPGA (Field-Programmable Gate Array) device, suitable for the implementation of biomedical signal processing. The proposal has been successfully validated by implementing an efficient QRS complex detector. The results show that, using a reduced amount of resources, values of sensitivity and positive predictive value above 99.49% are achieved, which make the proposed approach suitable for telemedicine applications.
AB - Over the last decades, many algorithms have been proposed for processing biomedical signals. Most of these algorithms have been focused on the elimination of noise and artifacts existing in these signals, so they can be used for automatic monitoring and/or diagnosis applications. With regard to remote monitoring, the use of portable devices often requires a reduced number of resources and power consumption, being necessary to reach a trade-off between the accuracy of algorithms and their computational complexity. This paper presents a SoC (System-on-Chip) architecture, based on a FPGA (Field-Programmable Gate Array) device, suitable for the implementation of biomedical signal processing. The proposal has been successfully validated by implementing an efficient QRS complex detector. The results show that, using a reduced amount of resources, values of sensitivity and positive predictive value above 99.49% are achieved, which make the proposed approach suitable for telemedicine applications.
UR - https://www.scopus.com/pages/publications/84953242619
U2 - 10.1109/EMBC.2015.7319763
DO - 10.1109/EMBC.2015.7319763
M3 - Chapter
C2 - 26737663
AN - SCOPUS:84953242619
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6018
EP - 6021
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -