点击次数：

影响因子：4.3

DOI码：10.1109/JSEN.2024.3356651

所属单位：University of Bremen; Nanjing University of Information Science and Technology

发表刊物：IEEE Sensors Journal

刊物所在地：UNITED STATES

关键字：artifact; biosignal; electrocardiogram; ECG; electrocardiography; pattern recognition; real-time system; signal quality

摘要：This article investigates electrocardiogram (ECG) acquisition artifacts often occurring in experiments due to human negligence or environmental influences, such as electrode detachment, misuse of electrodes, and unanticipated magnetic field interference, which are not easily noticeable by humans or software during acquisition. Such artifacts usually result in useless and irreparable signals; therefore, it would be a great help to research if the problems are detected during the acquisition process to alert experimenters instantly. We put forward a taxonomy of real-time artifacts during ECG acquisition, provide the simulation methods of each category, collect and share a 10-subject data corpus, and investigate machine learning (ML) solutions with a proposal of appropriate handcrafted features that reach an offline recognition rate of 90.89% in a five-best-output person-independent (PI) leave-one-out cross-validation (LOOCV). We also preliminarily validate the real-time applicability of our approach.

备注：ESI Hot Paper (top 0.1%) and Highly-Cited Paper (top 1%).
Dataset: https://www.uni-bremen.de/en/csl/research/sensorder-artifact-classification-during-biosignal-acquisition.

全部作者：Hui Liu*, Shiyao Zhang, Hugo Gamboa, Tingting Xue, Congcong Zhou, Tanja Schultz

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：24

期号：6

页面范围：9162-9171

ISSN号：1530-437X

是否译文：否

发表时间：2024-01-16

收录刊物：SCI

发布刊物链接：https://ieeexplore.ieee.org/document/10415350