Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.
作者： Haemwaan Sivaraks , Chotirat Ann Ratanamahatana
DOI: 10.1155/2015/453214
关键词:
摘要: … Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our …
hindawi.com
core.ac.uk 参考文章(63)
Alan Davies, Alwyn Scott, Principles of ECG Analysis Springer London. pp. 49- 61 ,(2014) , 10.1007/978-1-4471-4962-0_4
Heider Sanchez, Benjamin Bustos, Anomaly Detection in Streaming Time Series Based on Bounding Boxes Similarity Search and Applications. pp. 201- 213 ,(2014) , 10.1007/978-3-319-11988-5_19
Chotirat Ann Ratanamahatana, Eamonn Keogh, Everything you know about Dynamic Time Warping is Wrong ,(2004)
Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Three Myths about Dynamic Time Warping Data Mining. siam international conference on data mining. pp. 506- 510 ,(2005)
Mingwei Leng, Weiyi Yu, Shuai Wu, Hong Hu, Anomaly Detection Algorithm Based on Pattern Density in Time Series Springer, New York, NY. pp. 305- 311 ,(2013) , 10.1007/978-1-4614-7010-6_35
James Clifford, Donald J. Berndt, Using dynamic time warping to find patterns in time series knowledge discovery and data mining. pp. 359- 370 ,(1994)
Rishendra Verma, Rini Mehrotra, Vikrant Bhateja, An Improved Algorithm for Noise Suppression and Baseline Correction of ECG Signals Advances in Intelligent Systems and Computing. pp. 733- 739 ,(2013) , 10.1007/978-3-642-35314-7_83
André Paim Lemos, C. J. Tierra-Criollo, W. M. Caminhas, ECG Anomalies Identification Using a Time Series Novelty Detection Technique Springer, Berlin, Heidelberg. pp. 65- 68 ,(2007) , 10.1007/978-3-540-74471-9_16
Abdelhak M. Zoubir, Michael Muma, Falco Strasser, Motion artifact removal in ECG signals using multi-resolution thresholding european signal processing conference. pp. 899- 903 ,(2012) , 10.5281/ZENODO.42801
Ninh D. Pham, Quang Loc Le, Tran Khanh Dang, HOT aSAX: a novel adaptive symbolic representation for time series discords discovery asian conference on intelligent information and database systems. pp. 113- 121 ,(2010) , 10.1007/978-3-642-12145-6_12