D. Brossier, R. El Taani, M. Sauthier, N. Roumeliotis, G. Emeriaud, and P. Jouvet, "Creating a high-frequency electronic database in the picu: the perpetual patient," Pediatr. Crit. Care Med., vol. 19, no. 4, pp. e189-e198, 2018.
N. Roumeliotis, G. Parisien, S. Charette, E. Arpin, F. Brunet, and P. Jouvet, "Reorganizing care with the implementation of electronic medical records: a time-motion study in the picu," Pediatr. Crit. Care Med., vol. 19, no. 4, pp. e172-e179, 2018.
A. Mathieu and et al., "Validation process of a high-resolution database in a pediatric intensive care unit-describing the perpetual patient's validation," Journal of Evaluation in Clinical Practice, vol. 27, no. 2, pp. 316-324, 2021.
A. C. Dziorny and et al., "Clinical decision support in the picu: Implications for design and evaluation," Pediatr. Crit. Care Med., vol. 23, no. 8, pp. e392-e396, 2022.
T.-D. Le and et al., "Detecting of a patient's condition from clinical narratives using natural language representation," IEEE Open J. Eng. Med. Biol., vol. 3, pp. 142-149, 2022.
M. Sauthier, G. Tuli, P. A. Jouvet, J. S. Brownstein, and A. G. Randolph, "Estimated pao2: A continuous and noninvasive method to estimate pao2 and oxygenation index," Critical care explorations, vol. 3, no. 10, 2021.
G. Emeriaud, Y. M. López-Fernández, N. P. Iyer, M. M. Bembea, A. Agulnik, R. P. Barbaro, F. Baudin, A. Bhalla, W. B. De Carvalho, C. L. Carroll et al., "Executive summary of the second international guidelines for the diagnosis and management of pediatric acute respiratory distress syndrome (palicc-2)," Pediatr. Crit. Care Med., vol. 24, no. 2, p. 143, 2023.
P. Jouvet and et al., "A pilot prospective study on closed loop controlled ventilation and oxygenation in ventilated children during the weaning phase," Critical Care, vol. 16, no. 3, pp. 1-9, 2012.
M.Wysocki, P. Jouvet, and S. Jaber, "Closed loop mechanical ventilation," J. Clin. Monit. Comput., vol. 28, pp. 49-56, 2014.
B. L. Hill and et al., "Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning," Scientific reports, vol. 11, no. 1, p. 15755, 2021.
F. Fan and et al., "Estimating spo 2 via time-efficient high-resolution harmonics analysis and maximum likelihood tracking," IEEE J. Biomed. Health Inform., vol. 22, no. 4, pp. 1075-1086, 2017.
C. Macabiau, T.-D. Le, K. Albert, M. Shahriari, P. Jouvet, and R. Noumeir, "Label propagation techniques for artifact detection in imbalanced classes using photoplethysmogram signals," IEEE Access, vol. 12, pp. 81 221-81 235, 2024.
T.-D. Le, C. Macabiau, K. Albert, P. Jouvet, and R. Noumeir, "Grntransformer: Enhancing motion artifact detection in picu photoplethysmogram signals," arXiv preprint arXiv:2308.03722, 2023.
E. Khan, F. Al Hossain, S. Z. Uddin, S. K. Alam, and M. K. Hasan, "A robust heart rate monitoring scheme using photoplethysmographic signals corrupted by intense motion artifacts," IEEE Transactions on Biomedical engineering, vol. 63, no. 3, pp. 550-562, 2015.
D. Dao and et al., "A robust motion artifact detection algorithm for accurate detection of heart rates from photoplethysmographic signals using time-frequency spectral features," IEEE J. Biomed. Health Inform, vol. 21, no. 5, pp. 1242-1253, 2016.
P. Mehrgardt and et al., "Deep learning fused wearable pressure and ppg data for accurate heart rate monitoring," IEEE Sensors Journal, vol. 21, no. 23, pp. 27 106-27 115, 2021.
J. Liu and et al., "Pca-based multi-wavelength photoplethysmography algorithm for cuffless blood pressure measurement on elderly subjects," IEEE J. Biomed. Health Inform, vol. 25, no. 3, pp. 663-673, 2020.
D. A. Birrenkott and et al., "A robust fusion model for estimating respiratory rate from photoplethysmography and electrocardiography," IEEE Trans. Biomed. Eng., pp. 2033-2041, 2017.
B. Venema and et al., "Robustness, specificity, and reliability of an inear pulse oximetric sensor in surgical patients," IEEE J. Biomed. Health Inform, vol. 18, no. 4, pp. 1178-1185, 2013.
E. A. Alharbi and et al., "Non-invasive solutions to identify distinctions between healthy and mild cognitive impairments participants," IEEE J. Transl. Eng. Health Med., vol. 10, pp. 1-6, 2022.
C. Nwibor and et al., "Remote health monitoring system for the estimation of blood pressure, heart rate, and blood oxygen saturation level," IEEE Sensors Journal, vol. 23, no. 5, pp. 5401-5411, 2023.
Z. Wang and et al., "Time series classification from scratch with deep neural networks: A strong baseline," in International joint conference on neural networks, 2017, pp. 1578-1585.
D. Marzorati and et al., "Hybrid convolutional networks for end-to-end event detection in concurrent ppg and pcg signals affected by motion artifacts," IEEE Trans. Biomed. Eng., vol. 69, no. 8, 2022.
S. Maqsood and et al., "A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (ppg)," Ieee Access, vol. 9, pp. 138 817-138 833, 2021.
D. Hendrycks, M. Mazeika, S. Kadavath, and D. Song, "Using selfsupervised learning can improve model robustness and uncertainty," Advances in neural information processing systems, vol. 32, 2019.
T. Lin and et al., "A survey of transformers," AI Open, 2022.
T.-D. Le and et al., "Asmall-scale switch transformer and nlp-based model for clinical narratives classification," arXiv preprint arXiv:2303.12892, 2023.
R. Shwartz-Ziv, R. Balestriero, K. Kawaguchi, T. G. Rudner, and Y. LeCun, "An information-theoretic perspective on variance-invariance-covariance regularization," arXiv preprint arXiv:2303.00633, 2023.
J. Devlin and et al., "Bert: Pre-training of deep bidirectional transformers for language understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 4171-4186.
P. H. Le-Khac, G. Healy, and A. F. Smeaton, "Contrastive representation learning: A framework and review," Ieee Access, vol. 8, pp. 193 907-193 934, 2020.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, "A simple framework for contrastive learning of visual representations," in International conference on machine learning. PMLR, 2020, pp. 1597-1607.
K. Sohn, "Improved deep metric learning with multi-class n-pair loss objective," Advances in neural information processing systems, vol. 29, 2016.
A. v. d. Oord, Y. Li, and O. Vinyals, "Representation learning with contrastive predictive coding," arXiv preprint arXiv:1807.03748, 2018.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, "Unsupervised learning of visual features by contrasting cluster assignments," Advances in neural information processing systems, vol. 33, pp. 9912-9924, 2020.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, "Emerging properties in self-supervised vision transformers," in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9650-9660.
F. Pedregosa and et al, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
F. Chollet and et al., "keras," 2015.
D. Hunter and et al., "Selection of proper neural network sizes and architectures-a comparative study," IEEE Transactions on Industrial Informatics, vol. 8, no. 2, pp. 228-240, 2012.
M. Popel and et al., "Training tips for the transformer model," arXiv preprint arXiv:1804.00247, 2018.
N. Srivastava and et al., "Dropout: a simpleway to prevent neural networks from overfitting," The journal of machine learning research, vol. 15, no. 1, pp. 1929-1958, 2014.
X. Glorot and et al., "Understanding the difficulty of training deep feedforward neural networks," in Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2010, pp. 249-256.
S. Ioffe and et al., "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in International Conference on Machine Learning. PMLR, 2015, pp. 448-456.
N. Bjorck and et al., "Understanding batch normalization," Advances in Neural Information Processing Systems, vol. 31, 2018.
H. He and et al., "Adasyn: Adaptive synthetic sampling approach for imbalanced learning," in IEEE international joint conference on neural networks, 2008, pp. 1322-1328.
J. Azar and et al., "Deep recurrent neural network-based autoencoder for photoplethysmogram artifacts filtering," Computers & Electrical Engineering, vol. 92, p. 107065, 2021.
T.-D. Le, R. Noumeir, J. Rambaud, G. Sans, and P. Jouvet, "Adaptation of autoencoder for sparsity reduction from clinical notes representation learning," IEEE Journal of Translational Engineering in Health and Medicine, 2023.
Y. Liang, Z. Chen, G. Liu, and M. Elgendi, "A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in china," Scientific data, vol. 5, no. 1, pp. 1-7, 2018.
