A. Tolonen, L. Cluitmans, E. Smits, M. Gils, N. Maurits, R. Zietsma
{"title":"用书写和绘画任务的自动分析来区分帕金森病和其他引起震颤的综合征","authors":"A. Tolonen, L. Cluitmans, E. Smits, M. Gils, N. Maurits, R. Zietsma","doi":"10.1109/BIBE.2015.7367690","DOIUrl":null,"url":null,"abstract":"An easily performed and objective test of patients fine motor skills would be valuable in the diagnosis of Parkinson's disease (PD). In this study we present a set of automatic methods for quantifying the motor symptoms of PD and show that these automatically extracted features can be used to distinguish PD from other movement disorders causing tremor, namely essential tremor (ET), functional tremor (FT) and enhanced physiological tremor (EPT). The classification accuracies (mean of sensitivity and specificity) for separating PD from the other tremor syndromes were 82.0 % for ET, 69.8 % for FT and 72.2 % for EPT.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Distinguishing Parkinson's disease from other syndromes causing tremor using automatic analysis of writing and drawing tasks\",\"authors\":\"A. Tolonen, L. Cluitmans, E. Smits, M. Gils, N. Maurits, R. Zietsma\",\"doi\":\"10.1109/BIBE.2015.7367690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An easily performed and objective test of patients fine motor skills would be valuable in the diagnosis of Parkinson's disease (PD). In this study we present a set of automatic methods for quantifying the motor symptoms of PD and show that these automatically extracted features can be used to distinguish PD from other movement disorders causing tremor, namely essential tremor (ET), functional tremor (FT) and enhanced physiological tremor (EPT). The classification accuracies (mean of sensitivity and specificity) for separating PD from the other tremor syndromes were 82.0 % for ET, 69.8 % for FT and 72.2 % for EPT.\",\"PeriodicalId\":422807,\"journal\":{\"name\":\"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2015.7367690\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2015.7367690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Distinguishing Parkinson's disease from other syndromes causing tremor using automatic analysis of writing and drawing tasks
An easily performed and objective test of patients fine motor skills would be valuable in the diagnosis of Parkinson's disease (PD). In this study we present a set of automatic methods for quantifying the motor symptoms of PD and show that these automatically extracted features can be used to distinguish PD from other movement disorders causing tremor, namely essential tremor (ET), functional tremor (FT) and enhanced physiological tremor (EPT). The classification accuracies (mean of sensitivity and specificity) for separating PD from the other tremor syndromes were 82.0 % for ET, 69.8 % for FT and 72.2 % for EPT.