Abstract
Anomaly detection is a popular research in the age of Big Data. As a typical application scenario, anomaly detection over ECG data stream is confronted with particular difficulties including high real-time requirement and poor data quality. In this article, a novel method based on component spectrum is presented to provide a practicable solution for the problem. Experiments on real data show that the proposed method achieves high sensitivity, high specificity and low false alarm rate.
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This work was supported by Natural Science Foundation of China (No. 61170003).
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Wu, M., Qiu, Z., Hong, S., Li, H. (2016). Real-Time Anomaly Detection over ECG Data Stream Based on Component Spectrum. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_5
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DOI: https://doi.org/10.1007/978-3-319-45817-5_5
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