DFis, University of Aveiro, 3810-193 Aveiro, Portugal.
IEETA, DETI, LASI, University of Aveiro, 3810-193 Aveiro, Portugal.
Sensors (Basel). 2023 Jan 28;23(3):1443. doi: 10.3390/s23031443.
The adequate characterization of pain is critical in diagnosis and therapy selection, and currently is subjectively assessed by patient communication and self-evaluation. Thus, pain recognition and assessment have been a target of study in past years due to the importance of objective measurement. The goal of this work is the analysis of the electrocardiogram (ECG) under emotional contexts and reasoning on the physiological classification of pain under neutral and fear conditions. Using data from both contexts for pain classification, a balanced accuracy of up to 97.4% was obtained. Using an emotionally independent approach and using data from one emotional context to learn pain and data from the other to evaluate the models, a balanced accuracy of up to 97.7% was reached. These similar results seem to support that the physiological response to pain was maintained despite the different emotional contexts. Attempting a participant-independent approach for pain classification and using a leave-one-out cross-validation strategy, data from the fear context were used to train pain classification models, and data from the neutral context were used to evaluate the performance, achieving a balanced accuracy of up to 94.9%. Moreover, across the different learning strategies, Random Forest outperformed the remaining models. These results show the feasibility of identifying pain through physiological characteristics of the ECG response despite the presence of autonomic nervous system perturbations.
疼痛的充分特征对于诊断和治疗选择至关重要，目前通过患者沟通和自我评估来主观评估。因此，由于客观测量的重要性，疼痛识别和评估一直是过去几年的研究目标。这项工作的目的是分析情绪背景下的心电图（ECG），并推理中性和恐惧条件下疼痛的生理分类。使用来自这两种情况的数据进行疼痛分类，可获得高达 97.4%的平衡准确性。使用情绪独立的方法，并使用来自一种情绪情况的数据来学习疼痛，以及使用另一种情况的数据来评估模型，可以达到高达 97.7%的平衡准确性。这些相似的结果似乎表明，尽管存在不同的情绪背景，但对疼痛的生理反应得到了维持。尝试进行参与者独立的疼痛分类方法，并使用留一交叉验证策略，使用恐惧情况下的数据来训练疼痛分类模型，并使用中性情况下的数据来评估性能，可获得高达 94.9%的平衡准确性。此外，在不同的学习策略中，随机森林的表现优于其他模型。这些结果表明，尽管自主神经系统受到干扰，通过 ECG 反应的生理特征识别疼痛是可行的。
