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Artefact detection and quality assessment of ambulatory ECG signals

Authors
  • Moeyersons, Jonathan; 106013;
  • Smets, Elena; 91976;
  • Morales, John;
  • Villa, Amalia;
  • De Raedt, Walter;
  • Testelmans, Dries; 43440;
  • Buyse, Bertien; 34197;
  • Van Hoof, Chris; 12734;
  • Willems, Rik; 9279;
  • Van Huffel, Sabine; 13262;
  • Varon, Carolina; 73785;
Publication Date
Dec 01, 2019
Source
Lirias
Keywords
Language
English
License
Unknown
External links

Abstract

BACKGROUND AND OBJECTIVES: The presence of noise sources could reduce the diagnostic capability of the ECG signal and result in inappropriate treatment decisions. To mitigate this problem, automated algorithms to detect artefacts and quantify the quality of the recorded signal are needed. In this study we present an automated method for the detection of artefacts and quantification of the signal quality. The suggested methodology extracts descriptive features from the autocorrelation function and feeds these to a RUSBoost classifier. The posterior probability of the clean class is used to create a continuous signal quality assessment index. Firstly, the robustness of the proposed algorithm is investigated and secondly, the novel signal quality assessment index is evaluated. METHODS: Data were used from three different studies: a Sleep study, the PhysioNet 2017 Challenge and a Stress study. Binary labels, clean or contaminated, were available from different annotators with experience in ECG analysis. Two types of realistic ECG noise from the MIT-BIH Noise Stress Test Database (NSTDB) were added to the Sleep study to test the quality index. Firstly, the model was trained on the Sleep dataset and subsequently tested on a subset of the other two datasets. Secondly, all recording conditions were taken into account by training the model on a subset derived from the three datasets. Lastly, the posterior probabilities of the model for the different levels of agreement between the annotators were compared. RESULTS: AUC values between 0.988 and 1.000 were obtained when training the model on the Sleep dataset. These results were further improved when training on the three datasets and thus taking all recording conditions into account. A Pearson correlation coefficient of 0.8131 was observed between the score of the clean class and the level of agreement. Additionally, significant quality decreases per noise level for both types of added noise were observed. CONCLUSIONS: The main novelty of this study is the new approach to ECG signal quality assessment based on the posterior clean class probability of the classifier. / status: published

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