Statistical approach for lightweight detection of anomalies in ECG
作者： Ihor Vasyltsov , Changgyu Bak , Jiseung Jeong
DOI: 10.1109/EMBC.2016.7590674
关键词:
摘要: In this paper the application of statistical approach for anomaly detection in a wearable ECG monitor has been considered. The method is based on usage pre-defined expected behavior monitored biomedical signal and its on-line comparison with real-time measurements. Such can be implemented model signal. To test accuracy proposed ProSim 8 simulator was used to generate signals. Our experiments showed that critical anomalies ECG, such as Cardiac Failure, different types Arrhythmia, ST-segment deviations detected high precision (96∼100%), while false positive rate over typical NSR low (<4%). scalable terms required performance, power consumption, suitable implementation platform.
sci-hub.se 参考文章(17)
Oleg Starostenko, Vicente Alarcon-Aquino, Jorge Rodriguez-Asomoza, Oleg Sergiyenko, Vera Tyrsa, Remote Health/Vital Sign Monitoring Springer, Cham. pp. 221- 243 ,(2015) , 10.1007/978-3-319-12817-7_10
Rodrigo Varejão Andreão, Jérôme Boudy, Bernadette Dorizzi, Jean-Marc Boucher, Salim Graja, Statistical Models Based ECG Classification Springer Berlin Heidelberg. pp. 71- 93 ,(2009) , 10.1007/978-3-540-89506-0_4
Mirza Mansoor Baig, Hamid Gholamhosseini, Martin J. Connolly, mHealth Monitoring System for Hospitalised Older Adults – Current Issues and Challenges Springer, Cham. pp. 299- 317 ,(2015) , 10.1007/978-3-319-12817-7_14
S. Suave Lobodzinski, ECG patch monitors for assessment of cardiac rhythm abnormalities. Progress in Cardiovascular Diseases. ,vol. 56, pp. 224- 229 ,(2013) , 10.1016/J.PCAD.2013.08.006
Haemwaan Sivaraks, Chotirat Ann Ratanamahatana, Robust and accurate anomaly detection in ECG artifacts using time series motif discovery. Computational and Mathematical Methods in Medicine. ,vol. 2015, pp. 453214- 453214 ,(2015) , 10.1155/2015/453214
Mustapha M. Makki, Georges A. Saade, Albert G. Altouma, Sami Al-Terkawi, Abdulla Baobeid, Reza Tafreshi, Acquiring and analyzing electrocardiograms via smartphone to detect cardiovascular abnormalities biomedical and health informatics. pp. 277- 280 ,(2014) , 10.1109/BHI.2014.6864357
Adam Szczepański, Khalid Saeed, A Mobile Device System for Early Warning of ECG Anomalies Sensors. ,vol. 14, pp. 11031- 11044 ,(2014) , 10.3390/S140611031
Hao Ding, Kun-mean Hou, Jacques Lecoq, Gerard Bohner, Hai-ying Zhou, Hong Sun, Toward a Low Cost and Single Chip Holter: SoC-Holter Journal of Networks. ,vol. 6, pp. 181- 189 ,(2011) , 10.4304/JNW.6.2.181-189
S. Gradl, P. Kugler, C. Lohmuller, B. Eskofier, Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices international conference of the ieee engineering in medicine and biology society. ,vol. 2012, pp. 2452- 2455 ,(2012) , 10.1109/EMBC.2012.6346460
Qian Zheng, Chao Chen, Zhinan Li, Anpeng Huang, Bingli Jiao, Xiaohui Duan, Linzhen Xie, A novel multi-resolution SVM (MR-SVM) algorithm to detect ECG signal anomaly in WE-CARE project issnip biosignals and biorobotics conference biosignals and robotics for better and safer living. pp. 1- 6 ,(2013) , 10.1109/BRC.2013.6487453