Huang Mingxuan, Fu Cangcang, Chui Linbo, He Jiadong, Wang Xiaozhi, Luo Jikui, Wu Bin, Chen Yonggang, Hu Shaohua, Zhu Jihua, Li Yubo
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
Children's Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, China.
Heliyon. 2024 Dec 27;11(1):e41558. doi: 10.1016/j.heliyon.2024.e41558. eCollection 2025 Jan 15.
Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications.
This research established a pain recognition model based on AI skin potential (SP) signal analysis. A total of 237 subjects participated in this study, comprising 152 boys and 85 girls, ranging in age from 2 to 16 years old. Initially, we preprocessed SP signals and built datasets for pain and non-pain conditions, including 195 pain and 97 non-pain samples. Then, we applied wavelet transform (WT) to capture the time-frequency characteristics of the signals and extract energy features and created a feature set comprising 30 features and selected 10 most relevant ones using the "SelectKBest" function.We compared six algorithms, optimized their parameters, and evaluated the stability and fitting performance of each algorithm. The random forest (RF) algorithm emerged as the best, demonstrating significant performance in pain recognition with an accuracy of 80.3 % and a sensitivity of 92 %. The SP signals generated by children of different genders, ages, and needling positions during indwelling needle puncture were accurately recognized.
We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management.
儿童的临床疼痛表型复杂，且缺乏客观的生物学诊断标志物和认知模式。通过可穿戴设备检测生理信号可简化疾病诊断，并具有远程医疗应用的潜力。
本研究基于人工智能皮肤电位（SP）信号分析建立了疼痛识别模型。共有237名受试者参与本研究，其中包括152名男孩和85名女孩，年龄在2至16岁之间。最初，我们对SP信号进行预处理，并构建了疼痛和非疼痛状态的数据集，包括195个疼痛样本和97个非疼痛样本。然后，我们应用小波变换（WT）来捕捉信号的时频特征并提取能量特征，创建了一个包含30个特征的特征集，并使用“SelectKBest”函数选择了10个最相关的特征。我们比较了六种算法，优化了它们的参数，并评估了每种算法的稳定性和拟合性能。随机森林（RF）算法表现最佳，在疼痛识别方面具有显著性能，准确率为80.3%，灵敏度为92%。不同性别、年龄和留针穿刺部位的儿童在留针穿刺过程中产生的SP信号均能被准确识别。
我们开发了一个全面的SP识别模型，创新性地采用WT进行SP信号分析。这种时频分析方法通过保留低频特征，特别适合SP信号。通过将疼痛监测与SP信号和机器学习相结合，主观疼痛体验被转化为可量化的数据，实现了高精度和实时测量能力。这些优势为临床儿科疼痛管理提供了有价值的技术支持。
