{"title":"使用可穿戴心电图和血氧饱和度检测呼吸障碍","authors":"Yuezhou Zhang, Zhicheng Yang, Zhengbo Zhang, Peiyao Li, Desen Cao, Xiaoli Liu, Jiewen Zheng, Qian Yuan, Jianli Pan","doi":"10.1145/3274783.3275159","DOIUrl":null,"url":null,"abstract":"Conventional diagnosis using polysomnography (PSG) on breathing disorder is expensive and uncomfortable to patients. In this paper, we present a low-cost portable and wearable multi-sensor system to non-invasively acquire a subject's vital signs, and leverage various machine learning methods on features extracted from Electrocardiogram (ECG) and Blood oxygen saturation (SpO2) signals to detect breathing disorder events. Our preliminary predication accuracies on 110 clinical patients is 90.0%.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Breathing Disorder Detection Using Wearable Electrocardiogram And Oxygen Saturation\",\"authors\":\"Yuezhou Zhang, Zhicheng Yang, Zhengbo Zhang, Peiyao Li, Desen Cao, Xiaoli Liu, Jiewen Zheng, Qian Yuan, Jianli Pan\",\"doi\":\"10.1145/3274783.3275159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conventional diagnosis using polysomnography (PSG) on breathing disorder is expensive and uncomfortable to patients. In this paper, we present a low-cost portable and wearable multi-sensor system to non-invasively acquire a subject's vital signs, and leverage various machine learning methods on features extracted from Electrocardiogram (ECG) and Blood oxygen saturation (SpO2) signals to detect breathing disorder events. Our preliminary predication accuracies on 110 clinical patients is 90.0%.\",\"PeriodicalId\":156307,\"journal\":{\"name\":\"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3274783.3275159\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3274783.3275159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Breathing Disorder Detection Using Wearable Electrocardiogram And Oxygen Saturation
Conventional diagnosis using polysomnography (PSG) on breathing disorder is expensive and uncomfortable to patients. In this paper, we present a low-cost portable and wearable multi-sensor system to non-invasively acquire a subject's vital signs, and leverage various machine learning methods on features extracted from Electrocardiogram (ECG) and Blood oxygen saturation (SpO2) signals to detect breathing disorder events. Our preliminary predication accuracies on 110 clinical patients is 90.0%.
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