新生儿疼痛表情自动识别系统的研究进展

doi:10.3761/j.issn.0254-1769.2019.11.009

摘要/Abstract

摘要：

该文综述了新生儿疼痛表情数据库的建立、新生儿疼痛表情自动识别流程、新生儿疼痛表情自动识别系统的类型、疼痛表情识别系统的应用与不足,以期加强护士对新生儿疼痛表情识别系统的了解,为开展进一步研究提供参考依据。

关键词: 新生儿护理, 面部表情, 疼痛评估, 护理评估, 表情识别, 综述

Key words: Neonatal Nursing, Facial Expression, Pain Assessment, Nursing Assessment, Expression Recognition, Review

李梦婷, 陈朔晖. 新生儿疼痛表情自动识别系统的研究进展[J]. 中华护理杂志, 2019, 54(11): 1644-1647.

参考文献 40

[1]	王亚静, 李杨, 孙静 , 等. 新生儿重症监护病房患儿操作性疼痛现状调查[J]. 护理学杂志, 2019,34(11):20-23.
[2]	Orovec A, Disher T, Caddell K , et al. Assessment and management of procedural pain during the entire neonatal intensive care unit hospitalization[J]. Pain Manag Nurs, 2019.DOI: 10.1016/j.pmn.2018.11.016.
[3]	Walker SM . Translational studies identify long-term impact of prior neonatal pain experience[J]. Pain, 2017,158(4):S29-S42.
[4]	Watterberg KL, Cummings JJ, Benitz W E , et al. Prevention and management of procedural pain in the neonate:an update[J]. Pediatrics, 2016,137(2):e20154271-e20154283.
[5]	Boyle EM, Bradshaw J, Blake KI . Persistent pain in neonates:challenges in assessment without the aid of a clinical tool[J]. Acta Paediatr, 2018,107(1):63-67.
[6]	沈巧, 郑显兰, 林紫 , 等. 66家医疗机构儿童疼痛管理现状调查[J]. 中国护理管理, 2019,19(2):187-193.
[7]	Anand KJS, Eriksson M, Boyle EM , et al. Assessment of continuous pain in newborns admitted to NICUs in 18 European countries[J]. Acta Paediatr, 2017,106(8):1248-1259.
[8]	Pölkki T, Korhonen A, Laukkala H . Nurses’ perceptions of pain assessment and management practices in neonates:a cross-sectional survey[J]. Scand J Caring Sci, 2018,32(2):725-733.
[9]	Collados-Gómez L, Camacho-Vicente V, González-Villalba M , et al. Neonatal nurses’ perceptions of pain management[J]. Enferm Intensiva, 2018,29(1):41-47.
[10]	Zamzmi G, Kasturi R, Goldgof D , et al. A review of automated pain assessment in infants:features,classification tasks,and databases[J]. IEEE Rev Biomed Eng, 2018,11:77-96.
[11]	徐东娟, 王克芳 . 新生儿疼痛的评估工具[J]. 中华护理杂志, 2005,40(10):790-792.
[12]	Kappesser J, Kamper-Fuhrmann E, de Laffolie J , et al. Pain-specific reactions or indicators of a general stress response:investigating the discriminant validity of 5 well-established neo-natal pain assessment tools[J]. Clin J Pain, 2019,35(2):101-110.
[13]	Relland LM, Gehred A, Maitre NL . Behavioral and physiological signs for pain assessment in preterm and term neonates during a nociception-specific response:a systematic review[J]. Pediatr Neurol, 2019,90:13-23.
[14]	Kappesser J, de Laffolie J, Faas D , et al. Comparison of two neonatal pain assessment tools(children and infant’s postoperative pain scale and the neonatal facial coding system-revised)and their relations to clinicians’ intuitive pain estimates[J]. Eur J Pain, 2019,23(4):708-718.
[15]	Schiavenato M, Byers JF, Scovanner P , et al. Neonatal pain facial expression:evaluating the primal face of pain[J]. Pain, 2008,138(2):460-471.
[16]	Peters JW, Koot HM, Grunau RE , et al. Neonatal facial coding system for assessing postoperative pain in infants:item reduction is valid and feasible[J]. Clin J Pain, 2003,19(6):353-363.
[17]	Heiderich TM, Leslie AT, Guinsburg R . Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements[J]. Acta Paediatr, 2015,104(2):e63-e69.
[18]	柳毅 . 基于卷积神经网络的新生儿疼痛表情识别方法研究[D]. 南京:南京邮电大学, 2018.
[19]	蔡飞 . 基于三维卷积神经网络的新生儿疼痛表情识别[D]. 南京:南京邮电大学, 2018.
[20]	Brahnam S, Chuang CF, Sexton RS , et al. Machine assessment of neonatal facial expressions of acute pain[J]. Decis Support Syst, 2007,43(4):1242-1254.
[21]	Brahnam S, Nanni L McMurtrey S , et al. Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of local descriptors[J]. Appl Comput Inform, 2019. DOI: 10.1016/j.aci.2019.05.003.
[22]	朱金朵 . 基于深度学习的新生儿疼痛表情识别[D]. 南京:南京邮电大学, 2018.
[23]	Zhi RC, Zamzmi GZD, Goldgof D , et al. Automatic infants’ pain assessment by dynamic facial representation:effects of profile view,gestational age,gender,and race[J]. J Clin Med, 2018,7(7):E173.
[24]	Naik T, Mcgregor C, James A . Automated partial premature infant pain profile scoring using big data analytics [C]//1st International IEEE Life-Science Conference,IUltimo,Australia, 2017: 246-249.
[25]	Parodi E, Melis D, Boulard L , et al. Automated newborn pain assessment framework using computer vision techniques [C]//International Conference on Bioinformatics Research and Applications,Barcelona,Spain, 2017: 31-36.
[26]	Brahnam S, Chuang CF, Shih FY , et al. Machine recognition and representation of neonatal facial displays of acute pain[J]. Artif Intell Med, 2006,36(3):211-222.
[27]	Nanni L, Brahnam S, Lumini A . A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states[J]. Expert Syst Appl, 2010,37(12):7888-7894.
[28]	李玉静 . 基于Gabor小波变换和LBP结合的新生儿疼痛表情识别研究[D]. 南京:南京邮电大学, 2014.
[29]	余益团 . 基于LBP-top特征的新生儿疼痛表情识别研究[D]. 南京:南京邮电大学, 2016.
[30]	Fotiadou E, Zinger S, Tjon A Ten WE , et al. Video-based facial discomfort analysis for infants[C]// Conference on Visual Information Processing and Communication V, San Francisco, CA, 2014:90290F-90290F-14.
[31]	Gholami B, Haddad WM, Tannenbaum AR . Relevance vector machine learning for neonate pain intensity assessment using digital imaging[J]. IEEE Trans Biomed Eng, 2010,57(6):1457-1466.
[32]	Zamzami G, Ruiz G, Goldgof D , et al. Pain assessment in infants:towarsds spotting pain expression based on infants’ facial strain [C]//11th IEEE International Conference and Work-shops on Automatic Face and Gesture Recognition(FG),Ljubljana,Slovenia, 2015: 1-5.
[33]	卢官明, 李晓南, 李海波 . 新生儿疼痛面部表情识别方法的研究[J]. 光学学报, 2008,28(11):2109-2114.
[34]	李旭 . 基于分块加权局部二值模式的新生儿疼痛表情识别[D]. 南京:南京邮电大学, 2014.
[35]	卢官明, 石婉婉, 李旭 , 等. 基于LBP特征和稀疏表示的新生儿疼痛表情识别[J]. 南京邮电大学学报(自然科学版), 2015,35(1):19-25.
[36]	刘陶鸿 . 基于视频序列的新生儿疼痛表情识别系统的研究[D]. 南京:南京邮电大学, 2016.
[37]	Sun Y, Shan C, Tan T . Video-based discomfort detection for infants[J]. Mach Vision Appl, 2019,30(5):933-944.
[38]	彭进业, 杨瑞靖, 冯晓毅 , 等. 人脸疼痛表情识别综述[J]. 数据采集与处理, 2016,31(1):43-55.
[39]	谢萍, 周慧勤, 张灿 , 等. 全程模块化智能护理评估系统的应用与效果评价[J]. 中华护理杂志, 2017,52(7):863-866.
[40]	刘晓娜, 潘红英 . 护理决策支持系统的应用进展[J]. 中华护理杂志, 2018,53(6):735-739.

新生儿疼痛表情自动识别系统的研究进展

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