| Customizing the training dataset to an individual for improved heartbeat recognition performance in long-term ECG signals. | |
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MedLine Citation:
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PMID: 22255050 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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This work presents an investigation of the potential benefits of customizing the analysis of long-term ECG signals, collected from individuals using wearable sensors, by incorporating small amount of data from these individuals in the training set of our classifiers. The global training dataset selected was from the MIT-BIH Arrhythmias Database. This proposal is validated on long-term ECG recordings collected via wearable technology in unsupervised environments, as well on the MIT-BIH Normal Sinus Rhythm Database. Results illustrate that heartbeat classification performance could improve significantly if short periods of data (e.g., data from the first 5-minutes of every 2 hours) from the specific individual are regularly selected and incorporated into the global training dataset for training a customized classifier. |
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Authors:
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Can Ye; Johannes Pallauf; B V K Vijaya Kumar; Miguel Tavares Coimbra |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 2011 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2011 Aug |
Date Detail:
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Created Date: 2012-01-18 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 3322-5 Citation Subset: IM |
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Descriptor/Qualifier:
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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