Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (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. In this paper, we review the state of the art of applying Uncertainty Quantification to Machine Learning tasks in the biosignal domain. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. We address misconceptions in the field, provide recommendations for future work, and discuss gaps in the literature in relation to diagnostic implementations as well as control for prostheses or brain-computer interfaces. 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
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticEEGElectrocardiography (ECG)Electromyography (EMG)MisconceptionsUncertainty QuantificationSimilar Papers 제목 키워드 기반
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift
A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very…
BenchmarkingClassificationEEGElectroencephalogram (EEG)+1A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant fa…
Medical Image AnalysisUncertainty QuantificationA Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning
The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretabil…
Decision MakingDecision Making Under UncertaintyFairnessUncertainty QuantificationUncertainty Quantification in Case of Imperfect Models: A Review
Uncertainty quantification of complex technical systems is often based on a computer model of the system. As all models such a computer model is always wrong in the sense that it does not describe the reality perfectly. …
Uncertainty QuantificationParameter Estimation and Uncertainty Quantification for Systems Biology Models
Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods an…
Bayesian Inferenceparameter estimationUncertainty Quantification