Adaptive J-Wave Detection Architecture for Online BCG-Complex Recognition on FPGA

Publikationstyp
Conference Paper
Date Issued
2022-10
Sprache
English
Author(s)
Kulau, Ulf  
Richter, Christoph  
Rust, Jochen  
Institut
Smart Sensors E-EXK3  
TORE-URI
http://hdl.handle.net/11420/14533
Citation
29th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2022)
Contribution to Conference
29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022  
Publisher DOI
10.1109/ICECS202256217.2022.9970970
Scopus ID
2-s2.0-85145348260
In this paper a novel hardware architecture for high-accuracy and near-sensor BCG complex recognition is presented. Main contribution of our work is the implementation of an adaptive method for J-wave peak detection on FPGA enabling reliable online waveform monitoring. Also, to further increase the overall signal quality, Chebyshev-based filtering is installed, leading to a smoother signal progression. The evaluation results highlight our approach as a well suited solution for online BCG complex recognition in resource constraint environments, as detection rates between 95.31% and 100% can be achieved considering human bodies in a resting position.
Subjects
BCG
digital signal processing
medical devices
peak detection
SCG