ECG artefact detection using ensemble decision trees

This paper describes a novel method for artefact detection in electrocardiogram (ECG) signals. ECG analysis algorithms require a relatively clean dataset. Therefore, data corrupted by artefacts should either be filtered or discarded. The proposed method can be situated in the second class, since it identifies contaminated segments that can later be discarded from further analysis. The dataset used in this study contains 16 single lead ECG recordings, segmented in intervals of 60 seconds. Each segment is labeled either clean or contaminated by a medical doctor. Only 3.2% of the data is contaminated. The segments are characterized by features derived from their autocorrelation function (ACF). Due to its effectiveness in skewed datasets, the RUSBoost algorithm is then usedfor classification. Results show an accuracy of 99.85%, a sensitivity of 100% and a specificity of 95.51%. This suggests that the proposed method could be of great help for future ECG processing.

[1]  Johan A. K. Suykens,et al.  Optimized fixed-size kernel models for large data sets , 2010, Comput. Stat. Data Anal..

[2]  Joachim M. Buhmann,et al.  The Balanced Accuracy and Its Posterior Distribution , 2010, 2010 20th International Conference on Pattern Recognition.

[3]  S. Priori,et al.  Cost-effectiveness of neonatal ECG screening for the long QT syndrome. , 2006, European heart journal.

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[5]  Sabine Van Huffel,et al.  Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[7]  Hagen Malberg,et al.  CinC challenge — Assessing the usability of ECG by ensemble decision trees , 2011, 2011 Computing in Cardiology.

[8]  Jakub Ku.  Simple Scoring System for ECG Quality Assessment on Android Platform , 2011 .

[9]  H. K. Verma,et al.  RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis , 2008 .

[10]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  H. K. Verma,et al.  Artifacts and noise removal in electrocardiograms using independent component analysis. , 2008, International journal of cardiology.