Kulau, UlfUlfKulauRichter, ChristophChristophRichterRust, JochenJochenRust2023-01-102023-01-102022-1029th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2022)http://hdl.handle.net/11420/14533In 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.enBCGdigital signal processingmedical devicespeak detectionSCGConference Paper10.1109/ICECS202256217.2022.9970970Other