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

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.