A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering

TL;DR

This paper introduces a novel Plant Root System (PRS) algorithm inspired by swarm intelligence for feature engineering in biomedical signals, significantly improving classifier accuracy and potentially enhancing clinical diagnostic applications.

Contribution

The paper presents a new swarm intelligence-based feature extraction method that enhances classifier performance on biomedical signals, addressing limitations of traditional feature engineering.

Findings

PRS features have low correlation with traditional features.

Classifier accuracy improves substantially with PRS features.

The algorithm facilitates clinical application of biomedical signals.

Abstract

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.