A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation
Author(s) -
Yushun Gong,
Bihua Chen,
Yongqin Li
Publication year - 2013
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1260/2040-2295.4.2.185
Subject(s) - cardiopulmonary resuscitation , artifact (error) , resuscitation , medicine , computer science , intensive care medicine , artificial intelligence , emergency medicine
Various filtering strategies have been adopted and investigated to suppress the cardiopulmonary resuscitation (CPR) artifact. In this article, two types of artifact removal methods are reviewed: one is the method that removes CPR artifact using only ECG signals, and the other is the method with additional reference signals, such as acceleration, compression depth and transthoracic impedance. After filtering, the signal-to-noise ratio is improved from 0 dB to greater than 2.8 dB, the sensitivity is increased to > 90% as recommended by the American Heart Association, whereas the specificity was far from the recommended 95%, which is considered to be the major drawback of the available artifact removal methods. The overall performance of the adaptive filtering methods with additional reference signal outperforms the methods using only ECG signals. Further research should focus on the refinement of artifact filtering methods and the improvement of shock advice algorithms with the presence of CPR.
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