Subramaniam Saranya Devi, Doss Brindha, Chanderasekar Lakshmi Deepika, Madhavan Aswini, Rosary Antony Merlin
Department of Biomedical Engineering, PSG College of Technology, Coimbatore 641004, India.
Department of Electronics & Communication Engineering, PSG College of Technology, Coimbatore, 641004, India.
Healthc Technol Lett. 2018 Jul 17;5(4):124-129. doi: 10.1049/htl.2017.0108. eCollection 2018 Aug.
Pain is an unpleasant subjective experience. At present, clinicians are using self-report or pain scales to recognise and monitor pain in children. However, these techniques are not efficient to observe the pain in children having cognitive disorder and also require highly skilled observers to measure pain. Using these techniques it is also difficult to choose the analgesic drug dosages to the patients after surgery. Thus, this conceptual work explains the demand for automatic coding techniques to evaluate pain and also it documents some evidence of techniques that act as an alternative approach for objectively determining pain in children. In this review, some good indicators of pain in children are explained in detail; they are facial expressions from an RGB image, thermal image and also feature from well proven physiological signals such as electrocardiogram, skin conductance, body temperature, surgical pleth index, pupillary reflex dilation, analgesia nociception index, photoplethysmography, perfusion index etc.
疼痛是一种不愉快的主观体验。目前,临床医生使用自我报告或疼痛量表来识别和监测儿童的疼痛。然而,这些技术在观察患有认知障碍的儿童的疼痛方面效率不高,并且还需要训练有素的观察者来测量疼痛。使用这些技术,在手术后为患者选择镇痛药物剂量也很困难。因此,这项概念性工作解释了对自动编码技术来评估疼痛的需求,并且还记录了一些技术证据,这些技术可作为客观确定儿童疼痛的替代方法。在这篇综述中,详细解释了一些儿童疼痛的良好指标;它们是来自RGB图像、热图像的面部表情,以及来自经过充分验证的生理信号的特征,如心电图、皮肤电导率、体温、手术容积指数、瞳孔反射扩张、镇痛伤害感受指数、光电容积脉搏波描记术、灌注指数等。