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

目前临床上使用的疼痛评估方法信度、效度有限，耗时长，且仅适用于言语交际能力受限的患者。如果不能有效地测量疼痛，治疗疼痛可能会导致有风险的患者心脏应激，手术野灌流不足，止痛药使用过多或使用不足，以及在急性或慢性疼痛中滥用治疗的其他问题。该项目提案的主要目标是促进疼痛诊断和疼痛状态的监测。随着多模式传感器技术和高效数据分类的使用，可靠和有效的自动疼痛识别将成为可能。为了实现这一目标，将实验方案与新的强大的数据分析、模式识别和机器学习方法相结合，将是发展客观疼痛测量的一种有前途的策略。生物医学、视觉和音频数据将在健康对照组的实验控制条件下进行测量。测量后，将使用各种复杂的过滤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。这些特征是实时稳健的疼痛强度自动识别的输入。这是DFG项目的更新建议：基于面部表情和心理生物学参数的自动疼痛识别系统的发展和系统验证。本研究从以下几个关键方面推进了先前的研究项目：1.泛化：我们将通过扩展疼痛模式的复杂疼痛模型来测试和改进泛化：阶段性和紧张性、热性和按压。2.反应的特异性：将通过与心理社会压力的比较来描述疼痛识别系统的特异性。评估方式：除了心理生物学和面部参数外，我们还将评估副语言疼痛表达、皮肤温度、身体运动和其他疼痛识别方式。4.疼痛自动识别的跨时相可靠性将在(至少)一周后通过重复实验方案进行测试。5.我们将比较不同的特定人的校准方法，以提高识别率。所有疼痛分类器将被改装为在线处理，以促进临床有用的疼痛监测系统的发展。

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.