Advancing reliability and specificity of automatic multimodal recognition of pressure and heat pain intensit

提高压力和热痛强度自动多模式识别的可靠性和特异性

• 批准号: 193061652
• 负责人: Professor Dr.-Ing. Ayoub Al-Hamadi
• 金额: --
• 依托单位: Institut für Informations- und Kommunikationstechnik - IIKT
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/193061652?language=en
• 关键词: Advancing; reliability; specificity; automatic; multimodal

Currently used methods for clinical pain assessment have limited reliability, validity, are time consuming and can only limited applied to patients with restricted communicative verbal abilities. If valid measurement of the pain is not possible, treating the pain may lead to cardiac stress in risk patients, underperfusion of the operating field, over- or under-usage of analgesics and other problems of mistreatment in acute or chronic pain.Main goal of the project proposal is the advancement of pain diagnosis and monitoring of pain states. With the use of multimodal sensor technology and highly effective data classification, reliable and valid automated pain recognition will be possible. To reach this goal the combination of experimental protocols and new powerful methods of data analysis, pattern recognition and machine learning will be a promising strategy for the development of objective pain measurement. Biomedical, visual and audio data will be measured under experimentally controlled conditions in healthy controls. After measurement, the data will be pre-analyzed with a variety of complex filter and decomposition techniques to extract and select meaningful features. These features are the input for a robust automatic recognition of pain intensities in real-time.This is a renewal proposal of the DFG-Project: Advancement and Systematic Validation of an Automated Pain Recognition System on the Basis of Facial Expression and Psychobiological Parameters. It advances the previous project with the following key aspects:1. Generalizability: We will test and improve the generalizability with a complex pain model with extended pain modalities: phasic and tonic, heat and pressure. 2. Response specificity: The specificity of the pain recognition system will be described in comparison to psychosocial stress.3. Assessment modalities: In addition to psychobiological and facial parameters we will assess paralinguistic pain expressions, skin temperature, body movement and other modalities for pain recognition. 4. The trans-temporal reliability of automated pain recognition will be tested by repeating the experimental protocol after (at least) one week. 5. We will compare different person-specific calibration methods, which improve recognition rates.6. All pain classifiers will be adapted for online processing for the advancement of a clinically useful pain monitoring system.

目前临床疼痛评估方法的信度、效度有限，耗时长，且仅适用于语言交流能力有限的患者。如果无法有效测量疼痛，治疗疼痛可能会导致风险患者的心脏压力，手术区域灌注不足，镇痛药的过度或不足使用以及其他急性或慢性疼痛的治疗问题。该项目提案的主要目标是促进疼痛诊断和疼痛状态监测。通过使用多模态传感器技术和高效的数据分类，可靠有效的自动疼痛识别将成为可能。为了实现这一目标，实验协议和新的强大的数据分析，模式识别和机器学习的方法相结合，将是一个有前途的战略发展的客观疼痛测量。 将在实验控制条件下测量健康对照的生物医学、视觉和听觉数据。测量后，将使用各种复杂的滤波器和分解技术对数据进行预分析，以提取和选择有意义的特征。这些功能是输入一个强大的自动识别的疼痛强度在real-time.This是DFG-项目的更新提案：面部表情和心理生物学参数的基础上的自动疼痛识别系统的进步和系统验证。本论文主要从以下几个方面对前期课题进行了改进：1. 可推广性：我们将测试和改进的推广与复杂的疼痛模型与扩展的疼痛方式：相位和紧张，热和压力。2. 反应特异性：疼痛识别系统的特异性将与心理社会压力进行比较。 评估方式：除了心理生物学和面部参数，我们将评估非语言疼痛表达，皮肤温度，身体运动和其他形式的疼痛识别。4. 自动疼痛识别的跨时间可靠性将通过在（至少）一周后重复实验方案来测试。5. 我们将比较不同的个人特定的校准方法，提高识别率。 所有疼痛分类器将适用于在线处理，以推进临床有用的疼痛监测系统。