Special Issue: “Artificial Intelligence for Biomedical Signal Processing”
1. Introduction
2. Special Issue Articles
3. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial intelligence |
CNN | Convolutional neural network |
DL | Deep learning |
ECG | Electrocardiogram |
EEG | Electroencephalogram |
ESL | EEG source localization |
Grad-CAM | Gradient-weighted class activation mapping |
LSTM | Long short-term memory |
ML | Machine learning |
PPG | Photoplethysmography |
ResNetCNN | Residual convolutional neural network |
RGB | Red–green–blue |
RR | Respiratory rate |
RSBI | Rapid shallow breathing index |
SBTs | Spontaneous breathing trials |
SpO2 | Oxygen saturation |
STMaps | Spatial–temporal maps |
References
- Aminizadeh, S.; Heidari, A.; Dehghan, M.; Toumaj, S.; Rezaei, M.; Navimipour, N.J.; Stroppa, F.; Unal, M. Opportunities and Challenges of Artificial Intelligence and Distributed Systems to Improve the Quality of Healthcare Service. Artif. Intell. Med. 2024, 149, 102779. [Google Scholar] [CrossRef] [PubMed]
- Ahmadi, A.; Ganji, N.R. AI-Driven Medical Innovations: Transforming Healthcare through Data Intelligence. Int. J. BioLife Sci. 2023, 2, 132–142. [Google Scholar] [CrossRef]
- Ieracitano, C.; Zhang, X. Editorial Topical Collection: “Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment”. Bioengineering 2024, 11, 726. [Google Scholar] [CrossRef] [PubMed]
- Lian, S.; Luo, Z. Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for Bioengineering Special Issue: “Recent Advance of Machine Learning in Biomedical Image Analysis”. Bioengineering 2024, 11, 1106. [Google Scholar] [CrossRef] [PubMed]
- Kaviri, S.M.; Vinjamuri, R. Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation. Bioengineering 2024, 11, 967. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Sakevych, M.; Atkinson, G.; Metsis, V. BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis. Bioengineering 2024, 11, 299. [Google Scholar] [CrossRef] [PubMed]
- Cheng, C.H.; Yuen, Z.; Chen, S.; Wong, K.L.; Chin, J.W.; Chan, T.T.; So, R.H.Y. Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning. Bioengineering 2024, 11, 251. [Google Scholar] [CrossRef] [PubMed]
- Hwang, C.S.; Kim, Y.H.; Hyun, J.K.; Kim, J.H.; Lee, S.R.; Kim, C.M.; Nam, J.W.; Kim, E.Y. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering 2023, 10, 1222. [Google Scholar] [CrossRef] [PubMed]
- Park, J.E.; Kim, D.Y.; Park, J.W.; Jung, Y.J.; Lee, K.S.; Park, J.H.; Sheen, S.S.; Park, K.J.; Sunwoo, M.H.; Chung, W.Y. Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials. Bioengineering 2023, 10, 1163. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Q.; Liu, F.; Song, Y.; Fan, X.; Wang, Y.; Yao, Y.; Mao, Q.; Zhao, Z. Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model. Bioengineering 2023, 10, 1024. [Google Scholar] [CrossRef] [PubMed]
No. | Reference | Biomedical Signal | AI Methodology | Clinical Application |
---|---|---|---|---|
1 | Kaviri et al., 2024 [5] | EEG | Source localization + residual CNN | Motor imagery in post-stroke rehabilitation |
2 | Li et al., 2024 [6] | Multimodal (synthetic, accelerometer, and ECG signals) | Diffusion generative model | Biomedical signal synthesis |
3 | Chen et al., 2024 [7] | Facial videos (RGB) | CNN (ResNet-50, DenseNet-121, EfficientNet-B3) + feature map visualization | Remote SpO2 measurement and identification of relevant facial features |
4 | Hwang et al., 2023 [8] | PPG | Dilated residual neural network | Continuous respiratory rate estimation |
5 | Park et al., 2023 [9] | Numerical and waveform ventilatory parameters | Multimodal CNN + Grad-CAM | Weaning outcome prediction and relevant feature identification |
6 | Zhao et al., 2023 [10] | ECG + PPG | Transformer | Respiratory rate estimation |
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Vaquerizo-Villar, F.; Barroso-García, V. Special Issue: “Artificial Intelligence for Biomedical Signal Processing”. Bioengineering 2025, 12, 753. https://doi.org/10.3390/bioengineering12070753
Vaquerizo-Villar F, Barroso-García V. Special Issue: “Artificial Intelligence for Biomedical Signal Processing”. Bioengineering. 2025; 12(7):753. https://doi.org/10.3390/bioengineering12070753
Chicago/Turabian StyleVaquerizo-Villar, Fernando, and Verónica Barroso-García. 2025. "Special Issue: “Artificial Intelligence for Biomedical Signal Processing”" Bioengineering 12, no. 7: 753. https://doi.org/10.3390/bioengineering12070753
APA StyleVaquerizo-Villar, F., & Barroso-García, V. (2025). Special Issue: “Artificial Intelligence for Biomedical Signal Processing”. Bioengineering, 12(7), 753. https://doi.org/10.3390/bioengineering12070753