Comparison of signal preprocessing variants for machine learning-based CW radar heartbeat detection

The 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.

Subjects

Contactless vital-sign measurement

continuouswave (CW) radar

gated recurrent unit (GRU)

heartbeat

machine learning

preprocessing

DDC Class

610: Medicine, Health

621.38: Electronics, Communications Engineering

006.31: Machine Learning

Options

Comparison of signal preprocessing variants for machine learning-based CW radar heartbeat detection