Marcela Tobón-Cardona et al 2018 Physiol. Meas. 39 115010. https://doi.org/10.1088/1361-6579/aaecef
Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals
|Author:||Tobón-Cardona, Marcela1; Kenttä, Tuomas2; Porthan, Kimmo3,4;|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
3Division of Cardiology, Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland
4Department of Medicine, University of Helsinki and Minerva Foundation Institute for Medical Research, Helsinki, Finland
5National Institute for Health and Welfare, Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019052416912
|Publish Date:|| 2019-11-30
Objective: Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification.
Approach: The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM).
Main results: SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45 408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%. Significance: The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
Business Finland is gratefully acknowledged for the financial support in the VitalSens project.
© 2018 Institute of Physics and Engineering in Medicine. This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-6579/aaecef.