基于多模态特征集成算法的CID患者识别研究  

RESEARCH ON CID PATIENT CLASSIFICATION BASED ON MULTIMODAL FEATURE INTEGRATION ALGORITHM

作　　者：周文俊 欧静 龚亮 彭博[1] Zhou Wenjun;Ou Jing;Gong Liang;Peng Bo(School of Computer Science,Southwest Petroleum University,Chengdu 610000,Sichuan,China;Chengdu Second People's Hospital,Chengdu 610000,Sichuan,China)

机构地区：[1]西南石油大学计算机科学学院,四川成都610000 [2]成都市第二人民医院,四川成都610000

出　　处：《计算机应用与软件》2025年第4期142-149,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(82001803);四川省科技厅应用基础研究计划项目(2020YJ0197);成都市科技局项目(2021-YF05-00247-SN)。

摘　　要：目前,慢性失眠障碍(CID)患者数量逐年增加,及时诊断能有效避免CID患者症状加重。利用磁共振成像(MRI)技术结合分类算法可对CID患者进行识别。传统MRI数据分类算法基于单模态特征SVM算法进行,但该算法对CID患者数据分类效果不佳,因此,提出一种多模态特征集成算法进行CID患者识别以取得更好效果。多模态特征集成算法基于静息态功能MRI技术映射多模态特征,利用集成算法进行分类对比实验。实验结果显示,相较于传统MRI分类算法,多模态特征集成算法对CID患者数据分类效果更好,能有效识别CID患者,进而进行相关医疗辅助诊断工作。At present,the number of patients withchronic insomnia disorder(CID)is increasing year by year.Timely diagnosis can effectively avoid the aggravation of symptoms of CID patients.Magnetic resonance imaging(MRI)technology combined with a classification algorithm can be used to identify CID patients.The traditional MRI data classification algorithm is based on single-mode feature SVM algorithm,but this algorithm has poor effect on CID patient data classification.Therefore,a CID patient recognition algorithm based on multimodal feature integration is proposed to achieve better results.The multimodal feature integration algorithm mapped multimodal features based on resting-state functional MRI technology and used the integration algorithm for classification and comparison experiments.The experimental results show that,compared with the traditional MRI classification algorithm,the multimodal feature integration algorithm has better classification effect on CID patient data,and can effectively identify CID patients,to carry out relevant medical auxiliary diagnosis.

关 键 词：慢性失眠 患者分类 MRI 多模态特征 集成分类器 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]