College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
International Joint Innovation Center, Zhejiang University, Haining 314400, China.
Sensors (Basel). 2023 Jul 31;23(15):6815. doi: 10.3390/s23156815.
Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future.
疼痛管理是医学中的一个关键问题，尤其是对于可能难以有效表达疼痛的儿童来说。尽管医疗专业人员长期以来一直依赖各种评估量表，但这些工具已经显示出了局限性和主观性。在本文中，我们提出了一种基于皮肤电位信号的疼痛评估方案，旨在通过机器学习方法将主观疼痛转化为客观的疼痛识别指标。我们设计并实现了一种便携式非侵入性测量设备来测量皮肤电位信号，并进行了涉及 623 名受试者的实验。从实验数据中，我们选择了 358 条有效记录，然后将其分为 218 条无声样本和 262 条疼痛样本。从每个样本中提取了 38 个特征，其中有 7 个特征在疼痛识别中表现出优异的性能。我们使用三种分类算法发现，随机森林算法的准确率最高，达到 70.63%。虽然这种识别率表明其在临床应用中有一定的潜力，但需要注意的是，我们的结果与最新研究的识别率 81.5%有所不同。这是因为我们的疼痛刺激是由临床操作引起的，与电或热刺激相比，很难精确控制刺激强度。尽管存在这种局限性，但我们的疼痛评估方案在临床环境中提供客观疼痛识别方面具有显著的潜力。进一步研究和改进所提出的方法可能会在未来带来更准确和可靠的疼痛管理技术。
