Problem
Research questions and friction points this paper is trying to address.
Reviewing UQ methods for biosignal ML tasks
Addressing poor signal-to-noise in medical biosignals
Improving interpretability for clinical diagnostic applications
Uncertainty Quantification in Machine Learning for Biosignal Applications - A Review
🤖 AI Summary
Low signal-to-noise ratio (SNR) and stringent clinical interpretability requirements undermine model trustworthiness in biomedical machine learning applied to physiological signals (e.g., EEG, ECG, EOG, EMG).
Method: We conduct a systematic review of uncertainty quantification (UQ) techniques in biomedical ML, analyzing over ten UQ paradigms—including Bayesian deep learning, ensemble methods, confidence calibration, information entropy, and predictive variance—across multimodal biosignal scenarios. We further propose a clinically oriented UQ evaluation framework addressing methodology, quantitative metrics, and human-AI collaborative decision-making challenges.
Contribution/Results: This work identifies a critical research gap: the lack of clinical interaction validation for UQ methods in low-SNR biosignal contexts. It establishes theoretical foundations and practical guidelines for deploying safe, interpretable, and robust medical AI systems, bridging methodological advances with real-world clinical deployment requirements.
📝 Abstract
Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electroocculography (EOG) and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal to noise ratio, and good human interpretability is pivotal for medical applications and Brain Computer Interfaces. In this paper, we review the state of the art at the intersection of Uncertainty Quantification and Biosignal with Machine Learning. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty model in a (clinical) environment.
Innovation
Methods, ideas, or system contributions that make the work stand out.
UQ enhances ML interpretability in biosignals
Reviews UQ methods for EEG, ECG, EOG, EMG
Identifies gaps in clinical UQ implementations
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