Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection
•In this work is proposed a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes.•Experiments encompassing eight different classifiers with different approaches.•In order to select the best cla...
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Published in | Computer methods and programs in biomedicine Vol. 169; pp. 59 - 69 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Ireland
Elsevier B.V
01.02.2019
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Subjects | |
Online Access | Get full text |
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Summary: | •In this work is proposed a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes.•Experiments encompassing eight different classifiers with different approaches.•In order to select the best classifiers, a new methodology is proposed based on the Analytic Hierarchy Process.•The simulations include a balanced dataset built from artificial QRS complexes and ECG signals with QRS complexes misdetection.•Results obtained indicated that the proposed approach performed the best in terms of specificity (98.7%) and sensitivity (91.1%). Using balanced dataset, the results were improved to 99.5% and 98.5% in terms of the precision and sensitivity, respectively.
Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves).
Initially, a preprocessing stage based on wavelet denoising electrocardiogram signal scaling is applied. Then, the signal is segmented taking into account the ventricular depolarization timing and a new set of geometrical features are extracted. In order to validate this approach, simulations encompassing eight different classifiers are presented. To select the best classifiers, a new methodology is proposed based on the Analytic Hierarchy Process.
The best results, achieved with an Artificial Immune System, were 98.4%, 91.1% and 98.7% for accuracy, sensitivity and specificity, respectively. When artificial examples were generated to balance the dataset, the recognition performance increased to 99.0%, 98.5% and 99.5%, employing the Support Vector Machine classifier.
The proposed approach is compared with some of latest references and results indicate its effectiveness as a method for recognizing premature ventricular contraction. Besides, the overall system presents low computation load. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0169-2607 1872-7565 |
DOI: | 10.1016/j.cmpb.2018.12.028 |