Acute pain intensity monitoring with the classification of multiple physiological parameters.
Acute pain intensity monitoring with the classification of multiple physiological parameters.
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通过多个生理参数的分类，急性疼痛强度监测。
DOI：
10.1007/s10877-018-0174-8
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发表时间：
2019-06
影响因子：
2.2
通讯作者：
Liljeberg P
中科院分区：
文献类型：
--
作者：
Jiang M;Mieronkoski R;Syrjälä E;Anzanpour A;Terävä V;Rahmani AM;Salanterä S;Aantaa R;Hagelberg N;Liljeberg P
目前的急性疼痛强度评估工具主要基于患者的自我报告，这对于非交流性、镇静剂或危重患者是不切实际的。在先前的研究中，已经定性地观察到各种生理信号作为潜在的疼痛强度指数。在此基础上，本研究旨在开发一种多生理参数分类的连续疼痛监测方法。对30名健康志愿者在热痛和电痛刺激下的心率（HR）、呼吸率（BR）、皮肤电反应（GSR）和面肌电进行了检测。根据自我报告的视觉模拟量表，将采集的样本标记为无疼痛、轻度疼痛或中度/重度疼痛。首先从处理后的13个生理参数的分布中观察到这三类的模式。然后，人工神经网络分类器的训练，验证和测试的生理参数。平均分类准确率为70.6%。同样的方法被应用到每个类的中位数在每个测试和准确性提高到83.3%。在面肌电的情况下，该方法对新被试的适应性得到了提高，在留一被试交叉验证中，中重度疼痛的识别准确率从74.9 ± 21.0%提高到76.3 ± 18.1%。在健康志愿者中，GSR，HR和BR与疼痛强度变化的相关性优于面部肌肉活动。多个可访问的生理参数的分类可以潜在地提供一种区分无、轻度和中度/重度急性实验性疼痛的方法。
Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.
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影响因子：
10.7
作者：
Ledowski, T.;Ang, B.;Rhodes, J.
通讯作者：
Rhodes, J.
影响因子：
10.7
作者：
Ledowski, T.;Bromilow, J.;Schug, S. A.
通讯作者：
Schug, S. A.
影响因子：
8
作者：
Gjerstad, Ann Christin;Wagner, Kari;Storm, Hanne
通讯作者：
Storm, Hanne
影响因子：
4
作者：
Jepma, Marieke;Jones, Matt;Wager, Tor D.
通讯作者：
Wager, Tor D.
DOI：
10.1073/pnas.1430684100
发表时间：
2003-07-08
影响因子：
11.1
作者：
Coghill, RC;McHaffie, JG;Yen, YF
通讯作者：
Yen, YF