Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals

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http://hdl.handle.net/10138/311998

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Tobon-Cardona , M , Kenttä , T , Porthan , K , Tikkanen , J T , Oikarinen , L , Viitasalo , M , Salomaa , V , Huikuri , H , Junttila , J M & Seppänen , T 2018 , ' Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals ' , Physiological Measurement , vol. 39 , no. 11 , 115010 . https://doi.org/10.1088/1361-6579/aaecef

Title: Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals
Author: Tobon-Cardona, Marcela; Kenttä, Tuomas; Porthan, Kimmo; Tikkanen, Jani T.; Oikarinen, Lasse; Viitasalo, Matti; Salomaa, Veikko; Huikuri, Heikki; Junttila, Juhani M.; Seppänen, Tapio
Contributor: University of Helsinki, Kardiologian yksikkö
University of Helsinki, Clinicum
University of Helsinki, Kardiologian yksikkö
Date: 2018-12
Language: eng
Number of pages: 12
Belongs to series: Physiological Measurement
ISSN: 0967-3334
URI: http://hdl.handle.net/10138/311998
Abstract: 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 45408 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.
Subject: early repolarization
ECG
waveform prototype
supervised learning
STATISTICAL COMPARISONS
CLASSIFIERS
3121 General medicine, internal medicine and other clinical medicine
3111 Biomedicine
217 Medical engineering
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