Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multiple kernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.Comment: International Conference on Pattern Recognition (ICPR

Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation

Improper pain management can lead to severe physical or mental consequences, including suffering, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is challenging because different individuals experience pain differently. To overcome this, researchers have employed machine learning models to evaluate pain intensity objectively. However, these efforts have primarily focused on point estimation of pain, disregarding the inherent uncertainty and variability present in the data and model. Consequently, the point estimates provide only partial information for clinical decision-making. This study presents a neural network-based method for objective pain interval estimation, incorporating uncertainty quantification. This work explores three algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results reveal that LossS outperforms the other two by providing a narrower prediction interval. As LossS outperforms, we assessed its performance in three different scenarios for pain assessment: (1) a generalized approach (single model for the entire population), (2) a personalized approach (separate model for each individual), and (3) a hybrid approach (separate model for each cluster of individuals). Our findings demonstrate the hybrid approach's superior performance, with notable practicality in clinical contexts. It has the potential to be a valuable tool for clinicians, enabling objective pain intensity assessment while taking uncertainty into account. This capability is crucial in facilitating effective pain management and reducing the risks associated with improper treatment.Comment: 26 pages, 5 figures, 9 table

Multi-task neural networks for personalized pain recognition from physiological signals

Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain

A Personalized, Uncertainty-Aware, Trustworthy Algorithm for Effective Pain Assessment using Biosignals

Automatic pain assessment algorithms are used to improve pain assessment and assist subsequent pain treatment and management for patients without healthcare provider supervision. This thesis proposes a new pain assessment framework called "A Personalized, Uncertainty-Aware, Trustworthy Algorithm for Effective Pain Assessment using Biosignals." The framework takes into account the uncertainty of the data itself and the strong subjectivity of the pain experience, utilizing heart rate variability analysis to assess data uncertainty and test time adaptation to deal with distribution drift. It considers that pain data is imperfect, that there are data-label inconsistencies, and that the personalization of pain prediction algorithms is important. Our aim is to create complete frameworks for automated pain assessment that reduce the complexity of algorithms while predicting well. We collected experimental pain data and data from real pain patients, including post-surgical patients and women in labor. Through experiments and analyses, the framework outperforms state-of-the-art methods