Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya
{"title":"超短皮肤电活动信号自动疼痛评估","authors":"Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya","doi":"10.1145/3555776.3577721","DOIUrl":null,"url":null,"abstract":"Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Pain Assessment with Ultra-short Electrodermal Activity Signal\",\"authors\":\"Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya\",\"doi\":\"10.1145/3555776.3577721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.\",\"PeriodicalId\":42971,\"journal\":{\"name\":\"Applied Computing Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computing Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3555776.3577721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555776.3577721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Automatic Pain Assessment with Ultra-short Electrodermal Activity Signal
Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.