Bridging Signal Intelligence and Clinical Insight: A Comprehensive Review of Feature Engineering, Model Interpretability, and Machine Learning in Biomedical Signal Analysis

Biomedical signal analysis underpins modern healthcare by enabling accurate diagnosis, continuous physiological monitoring, and informed patient management. While deep learning excels at automated feature extraction and end-to-end modeling, classical ML remains essential for tasks requiring interpretability, data efficiency, and clinical transparency. This review synthesizes advances in ML methods including Support Vector Machines, Random Forests, and Decision Trees focusing on physiologically informed feature engineering, robust feature selection, and meaningful model interpretation. We provide guidelines for signal preprocessing, domain-specific feature extraction, and selection strategies across standard biomedical signals such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), Electrovestibulography (EVestG), and tracheal breathing sounds (TBSs). Reviewing TBS studies illustrates an end-to-end workflow highlighting common features and classifiers alongside practical challenges and solutions. Reported ML application performance ranges from 85 to 94% accuracy for EEG, ECG, and EMG, to 82% specificity for TBSs, emphasizing the trade-off between interpretability and predictive performance. Marginal accuracy gains alone do not constitute meaningful progress unless they enhance clinical insight, actionable decision-making, or model transparency. Finally, we compare ML with DL, discuss strengths and limitations, and provide recommendations and future directions for developing robust, interpretable, and clinically relevant biomedical ML.