Wavelet-based artificial intelligence in healthcare is primarily used for feature extraction from biosignals, with classification accuracies frequently exceeding 95%.
Systematic Review (n=112)
Wavelet-based AI techniques, particularly discrete wavelet transforms combined with deep learning, demonstrate high accuracy (>95%) for healthcare applications including cardiac abnormality detection, though clinical validation remains a challenge.
ABSTRACT The integration of wavelet transformation and artificial intelligence techniques has demonstrated significant potential in healthcare applications. Wavelet analysis enables multi‐scale signal decomposition and feature extraction that, when combined with machine and deep learning approaches, enhance the accuracy and efficiency of medical data analysis. This systematic review synthesizes 112 relevant studies from 2013 to 2023 exploring wavelet‐based artificial intelligence in healthcare. Our analysis reveals that the discrete wavelet transform dominates (43% of studies), primarily used for feature extraction from biosignals (82%) and medical images. Major applications include cardiac abnormality detection (29%), neurological disorder diagnosis (27%), and mental health assessment (16%), with classification accuracies frequently exceeding 95%. Key findings indicate a shift from traditional machine learning to deep learning approaches after 2020, with emerging trends in hybrid architectures. The review identifies critical challenges in computational efficiency, optimal wavelet selection, and clinical validation. Future developments should focus on real‐time processing optimization, interpretable deep learning models, multi‐modal data fusion, and validation on larger clinical datasets, advancing the translation of these systems into practical clinical tools.
Shuvo et al. (2025) conducted a systematic review in Healthcare applications (cardiac, neurological, mental health) (n=112). Wavelet transformation and artificial intelligence techniques was evaluated on Application areas and classification accuracy. Wavelet-based artificial intelligence in healthcare is primarily used for feature extraction from biosignals, with classification accuracies frequently exceeding 95%.