{"title":"读者须知","authors":"","doi":"10.1109/lgrs.2013.2256414","DOIUrl":null,"url":null,"abstract":"—An adapted state-of-the-art method of processing information known as Reservoir Computing is used to show its utility on the open and time-consuming problem of heartbeat classifi-cation. The MIT-BIH arrhythmia database is used following the guidelines of the Association for the Advancement of Medical In-strumentation. Our approach requires a computationally inexpensive preprocessing of the electrocardiographic signal leading to a fast algorithm and approaching a real-time classification solution. Our multiclass classification results indicate an average specificity of 97.75% with an average accuracy of 98.43%. Sensitivity and positive predicted value show an average of 84.83% and 88.75%, respectively, what makes our approach significant for its use in a clinical context.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"2 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"109","resultStr":"{\"title\":\"Notice to the reader\",\"authors\":\"\",\"doi\":\"10.1109/lgrs.2013.2256414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"—An adapted state-of-the-art method of processing information known as Reservoir Computing is used to show its utility on the open and time-consuming problem of heartbeat classifi-cation. The MIT-BIH arrhythmia database is used following the guidelines of the Association for the Advancement of Medical In-strumentation. Our approach requires a computationally inexpensive preprocessing of the electrocardiographic signal leading to a fast algorithm and approaching a real-time classification solution. Our multiclass classification results indicate an average specificity of 97.75% with an average accuracy of 98.43%. Sensitivity and positive predicted value show an average of 84.83% and 88.75%, respectively, what makes our approach significant for its use in a clinical context.\",\"PeriodicalId\":13046,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Letters\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"109\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/lgrs.2013.2256414\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/lgrs.2013.2256414","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
—An adapted state-of-the-art method of processing information known as Reservoir Computing is used to show its utility on the open and time-consuming problem of heartbeat classifi-cation. The MIT-BIH arrhythmia database is used following the guidelines of the Association for the Advancement of Medical In-strumentation. Our approach requires a computationally inexpensive preprocessing of the electrocardiographic signal leading to a fast algorithm and approaching a real-time classification solution. Our multiclass classification results indicate an average specificity of 97.75% with an average accuracy of 98.43%. Sensitivity and positive predicted value show an average of 84.83% and 88.75%, respectively, what makes our approach significant for its use in a clinical context.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.