ArticleSensors (Basel, Switzerland)2025
A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions.World journal of methodology · 2026Review
- Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trial.NPJ digital medicine · 2026Article
- Deep Learning and Noninvasive Sensors for Detecting Physiological Dysregulation: A Scoping Review.Journal of medical systems · 2026Review
- Cross-modal synchronization of EEG and ECG reveals hidden signatures of recovery in traumatic brain injury.Journal of neuroengineering and rehabilitation · 2026Article
- Precision perioperative AI: from signals, images, and records to applications in anesthesia-a narrative mini-review proposing an operational framework.Frontiers in medicine · 2026Review
- Article
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
- Artificial intelligence in perioperative medicine education: A feasibility test of case-based learning.Journal of perioperative practiceArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), photoplethysmography (PPG), and electrocardiogram (ECG) signals to predict nociception. We collected data from patients undergoing general anesthesia at two hospitals and developed and compared two deep learning models: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) network. Both models were trained on expert anesthesiologists' assessments of nociception. We evaluated normalization strategies for offline and online usage and found that Min-Max normalization was most effective for our dataset. Our results demonstrate that the MLP model accurately captured nociceptive changes in response to painful surgical stimuli, whereas the LSTM model provided smoother predictions but with lower sensitivity to rapid changes. These findings underscore the potential of multimodal, deep learning-based solutions to improve real-time nociception monitoring in diverse clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.