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Physiological Signal-Based Method for Measurement of Pain Intensity
Chu YQ(褚亚奇); Zhao XG(赵新刚); Han JD(韩建达); Su, Yang
Department机器人学研究室
Source PublicationFRONTIERS IN NEUROSCIENCE
ISSN1662-453X
2017
Volume11Pages:1-13
Indexed BySCI
WOS IDWOS:000406527800001
Contribution Rank1
Funding OrganizationNational Nature Science Foundation of China [61273355, 61573340] ; National High Technology Research and Development Program of China (863 Program) [2015AA042301] ; Youth Innovation Promotion Association CAS
KeywordFeature Extraction Feature Selection And Reduction Pain Intensity Quantification Physiological Signals Pattern Classification
AbstractThe standard method for prediction of the absence and presence of pain has long been self-report. However, for patients with major cognitive or communicative impairments, it would be better if clinicians could quantify pain without having to rely on the patient's self-description. Here, we present a newly pain intensity measurement method based on multiple physiological signals, including blood volume pulse (BVP), electrocardiogram (ECG), and skin conductance level (SCL), all of which are induced by external electrical stimulation. The proposed pain prediction system consists of signal acquisition and preprocessing, feature extraction, feature selection and feature reduction, and three types of pattern classifiers. Feature extraction phase is devised to extract pain-related characteristics from short-segment signals. A hybrid procedure of genetic algorithm-based feature selection and principal component analysis-based feature reduction was established to obtain high-quality features combination with significant discriminatory information. Three types of classification algorithms linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine are adopted during various scenarios, including multi-signal scenario, multi-subject and between-subject scenario, and multi-day scenario. The classifiers gave correct classification ratios much higher than chance probability, with the overall average accuracy of 75% above for four pain intensity. Our experimental results demonstrate that the proposed method can provide an objective and quantitative evaluation of pain intensity. The method might be used to develop a wearable device that is suitable for daily use in clinical settings.
Language英语
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
WOS SubjectNeurosciences
WOS KeywordHEART-RATE-VARIABILITY ; COMPONENT ANALYSIS ; SKIN-CONDUCTANCE ; CARE ; GUIDELINES ; ADULTS ; TOOLS
WOS Research AreaNeurosciences & Neurology
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20811
Collection机器人学研究室
Corresponding AuthorZhao XG(赵新刚)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Shengjing Hospital of China Medical University, Shenyang, China
Recommended Citation
GB/T 7714
Chu YQ,Zhao XG,Han JD,et al. Physiological Signal-Based Method for Measurement of Pain Intensity[J]. FRONTIERS IN NEUROSCIENCE,2017,11:1-13.
APA Chu YQ,Zhao XG,Han JD,&Su, Yang.(2017).Physiological Signal-Based Method for Measurement of Pain Intensity.FRONTIERS IN NEUROSCIENCE,11,1-13.
MLA Chu YQ,et al."Physiological Signal-Based Method for Measurement of Pain Intensity".FRONTIERS IN NEUROSCIENCE 11(2017):1-13.
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