Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
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Volume 6 Number 1 2024
REVIEWS (Open Access)
Artificial Intelligence and Machine Learning in Biomedical Signal Analysis: Deep Learning Performance, Clinical Validation, and Systematic Review
Yue Li 1, Shunqi Liu 2 *
Bioinfo Chem 6 (1) 1-13 https://doi.org/10.25163/bioinformatics.6110583
Submitted: 17 October 2024 Revised: 01 December 2024 Accepted: 12 December 2024 Published: 14 December 2024
Abstract
Artificial intelligence (AI) and machine learning are increasingly transforming biomedical signal analysis, particularly across modalities such as electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), and respiratory signals. While traditional signal-processing approaches relied heavily on manual interpretation, contemporary methods are now driven by data-intensive inference and deep learning architectures. This systematic review synthesizes current evidence on model performance, methodological consistency, and clinical validation in AI-based biomedical signal analysis. Across the literature, deep learning models—especially convolutional and hybrid architectures—consistently demonstrate high predictive performance, with reported accuracies approaching 97–99% in EEG and ECG applications. However, these results are not uniformly observed. Model performance is strongly influenced by dataset size, diversity, and validation strategies, with smaller or homogeneous datasets often yielding overly optimistic estimates. A substantial proportion of studies lack external validation, highlighting a persistent gap between algorithmic performance and real-world clinical applicability. Emerging domains, including EMG and respiratory signal analysis, further illustrate variability in model robustness and susceptibility to bias. Taken together, these findings suggest that while artificial intelligence and machine learning offer significant potential for advancing biomedical signal analysis, their clinical reliability depends on rigorous validation, standardized reporting, and diverse datasets. Bridging the gap between high performance and clinical translation remains essential for meaningful implementation.
Keywords: Biomedical signals; Artificial intelligence; Machine learning; Deep learning; EEG; ECG; EMG; Systematic review
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