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Item type:Publication, Access status: without files , Comparison of signal preprocessing variants for machine learning-based CW radar heartbeat detectionThe performance of continuous-wave (CW) radarbased non-contact cardiac monitoring strongly depends on the utilized signal representation and preprocessing. The received radar measurements can be represented as in-phase and quadrature (I/Q), phase, or magnitude signals. In addition, various preprocessing strategies have been proposed to enhance heartbeatrelated components, including bandpass filtering in the pulsewave and heart-sound frequency ranges, and the application of first- and second-order derivatives. By systematically comparing these preprocessing variants across the different signal representations, this study aims to identify which variants and combinations are most suitable for machine learning-based heartbeat detection under consistent evaluation conditions. The highest mean F1 score among individual features (94.99%) was obtained with the second derivative of I/Q signals and was slightly increased to 95.08% by combining multiple preprocessing variants.Publicationtype: Conference Paper36