  To date, very few biomedical signals have transitioned from research
applications to clinical applications. This is largely due to the lack of trust
in the diagnostic ability of non-stationary signals. To reach the level of
clinical diagnostic application, classification using high-quality signal
features is necessary. While there has been considerable progress in machine
learning in recent years, especially deep learning, progress has been quite
limited in the field of feature engineering. This study proposes a feature
extraction algorithm based on group intelligence which we call a Plant Root
System (PRS) algorithm. Importantly, the correlation between features produced
by this PRS algorithm and traditional features is low, and the accuracy of
several widely-used classifiers was found to be substantially improved with the
addition of PRS features. It is expected that more biomedical signals can be
applied to clinical diagnosis using the proposed algorithm.
