Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor.
Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor.
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DOI：
10.3390/s21123956
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发表时间：
2021-06-08
期刊：
影响因子：
--
通讯作者：
Chon KH
中科院分区：
文献类型：
--
作者：
Kong Y;Posada-Quintero HF;Chon KH
疼痛的主观性会导致不准确的止痛药处方，从而加剧药物成瘾和过量用药。鉴于疼痛通常是在患者家中经历的，因此迫切需要能够实时量化疼痛的移动设备。我们在我们的智能手机应用程序中实现了三个时域和频域皮肤电活动(EDA)指数，该应用程序使用手腕上的设备收集EDA信号。然后，我们使用来自10个受试者的热格栅数据来评估我们的计算算法。热烤架提供的疼痛程度是根据视觉模拟评分(VAS)为每个受试者校准的8分(满分10分)。此外，我们使用从另一组接受电脉冲疼痛刺激的15名受试者预先收集的数据集来模拟对智能手机应用程序的实时处理，得到的VAS疼痛评分为7分(满分10分)。所有的EDA特征在无痛节段和疼痛节段之间都显示出显著的差异，称为每个疼痛刺激前后的5-S节段。随机森林法检测疼痛的准确率最高，为81.5%，留一人交叉验证法的灵敏度为78.9%，特异度为84.2%。我们的结果显示了智能手机应用程序提供近乎实时的客观疼痛检测的潜力。
The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.
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