SCH: INT: Collaborative Research: Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS)

SCH：INT：协作研究：连续目标多模式疼痛评估传感系统（COMPASS）的新颖计算方法

• 批准号: 1838796
• 负责人: Yingzi Lin
• 金额: $ 61.39万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838796&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Novel

Few objective pain assessment techniques are currently available for use in clinical settings. Clinicians typically use subjective pain scales for pain assessment and management, which has resulted in suboptimal treatment plans, delayed responses to patient needs, over-prescription of opioids, and drug-seeking behavior among patients. This project will investigate science-based methods to build a robust Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) and a clinical interface capable of generating objective measurements of pain from multimodal physiological signals and facial expressions. COMPASS will allow objective measurements that can be used to significantly improve pain assessment, pain management strategies, reduce opioid dependency, and advance the field of pain-related research. The educational plan will include activities to engage patient training, K-12 students, minorities and underrepresented groups, as well as general public. These outcomes will also lead to development of a diverse work force needed to support advanced medical technologies and services.Using advanced biosensing systems, data fusion algorithms and machine learning models, this project will develop a robust, reliable, and accurate pain intensity classification system, COMPASS, for estimating pain intensity experienced by patients in real-time on a 0-10 scale, which is the standard scale used by physicians in clinical settings. In the initial phase of the project, the team will conduct a pilot at Brigham and Women's Hospital to experiment with the different elements for developing the sensing systems and collect data to develop data fusion algorithms and machine learning models. In the later phase of the project, the team will collect an extensive set of data to train and validate the fully implemented COMPASS. Physiological sensor data from electroencephalograph, facial-expression, patient self-reported pain scales, and physician/nurse assessed pain scales will be collected from the subjects as they experience pain modulated by medical therapies that cause patients pain. The project will investigate evidence-based machine learning and feature extraction methods for physiological signals and facial-expression images. This highly interdisciplinary research will make significant contributions to the areas of pain assessment and management, human factors and patient safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

目前很少有客观的疼痛评估技术可用于临床环境。临床医生通常使用主观疼痛量表进行疼痛评估和管理，这导致了次优的治疗计划，对患者需求的延迟反应，阿片类药物的过度处方以及患者的药物寻求行为。该项目将研究以科学为基础的方法，以建立一个强大的连续客观多模态疼痛评估传感系统（COMPASS）和一个临床界面，能够从多模态生理信号和面部表情中生成疼痛的客观测量。COMPASS将允许客观的测量，可用于显着改善疼痛评估，疼痛管理策略，减少阿片类药物依赖，并推进疼痛相关研究领域。教育计划将包括让病人培训、K-12学生、少数民族和代表性不足的群体以及公众参与的活动。这些成果也将导致发展一个多样化的劳动力需要支持先进的医疗技术和服务。使用先进的生物传感系统，数据融合算法和机器学习模型，该项目将开发一个强大的，可靠的，准确的疼痛强度分类系统，COMPASS，用于估计疼痛强度的患者在0-10的规模实时，这是医生在临床环境中使用的标准量表。在项目的初始阶段，该团队将在布里格姆妇女医院进行试点，试验开发传感系统的不同元素，并收集数据以开发数据融合算法和机器学习模型。在项目的后期阶段，该团队将收集大量数据，以培训和验证全面实施的COMPASS。将从受试者中收集来自脑电图、面部表情、患者自我报告的疼痛量表和医生/护士评估的疼痛量表的生理传感器数据，因为受试者经历了由导致患者疼痛的药物治疗调节的疼痛。该项目将研究基于证据的机器学习和生理信号和面部表情图像的特征提取方法。这项高度跨学科的研究将为疼痛评估和管理、人为因素和患者安全等领域做出重大贡献。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

An experimental study of objective pain measurement using pupillary response based on genetic algorithm and artificial neural network

• DOI: 10.1007/s10489-021-02458-4
• 发表时间: 2021-05-17
• 期刊: APPLIED INTELLIGENCE
• 影响因子: 5.3
• 作者: Wang, Li;Guo, Yikang;Lin, Yingzi
• 通讯作者: Lin, Yingzi

Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG

• DOI: 10.1016/j.neucom.2019.10.023
• 发表时间: 2020-02
• 期刊: Neurocomputing
• 影响因子: 6
• 作者: Mingxin Yu;Yichen Sun;Bofei Zhu;Lianqing Zhu;Yingzi Lin;Xiaoying Tang;Yikang Guo;Guangkai Sun;M. Dong

Cold pressor pain assessment based on EEG power spectrum

基于EEG功率谱的冷压疼痛评估

• DOI: 10.1007/s42452-020-03822-8
• 发表时间: 2020
• 期刊: SN Applied Sciences
• 影响因子: 2.6
• 作者: Wang, Li;Xiao, Yan;Urman, Richard D.;Lin, Yingzi
• 通讯作者: Lin, Yingzi

EEG-based tonic cold pain assessment using extreme learning machine

• DOI: 10.3233/ida-184388
• 发表时间: 2020
• 期刊: Intell. Data Anal.
• 作者: Mingxin Yu;Hao Yan;Jing Han;Yingzi Lin;Lianqing Zhu;Xiaoying Tang;Guangkai Sun;Yanlin He;Yikang Guo