Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals
Authors:
- Turker Tuncer,
- Sengul Dogan,
- Paweł Pławiak,
- U. Rajendra Acharya
Abstract
Electrocardiography (ECG) is widely used for arrhythmia detection nowadays. The machine learning methods with signal processing algorithms have been used for automated diagnosis of cardiac health using ECG signals. In this article, discrete wavelet transform (DWT) coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection. The ECG signals of 10 s duration are subjected to DWT to decompose up to five levels. The 1D-HLP extracts 512 dimensional features from each level of the five levels of low pass filter. Then, these extracted features are concatenated to obtain 512×6=3072 dimensional feature set. These fused features are subjected to neighborhood component analysis (NCA) feature reduction technique to obtain 64, 128 and 256 features. Finally, these features are subjected to 1 nearest neighborhood (1NN) classifier for classification with 4 distance metrics namely city block, Euclidean, spearman and cosine. We have obtained a classification accuracy of 95.0% in classifying 17 arrhythmia classes using MIT-BIH Arrhythmia ECG dataset. Our results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmia detection using ECG signals.
- Record ID
- CUT124aa9cbf3294c218921e3cb9834d88a
- Publication categories
- ;
- Author
- Journal series
- Knowledge-Based Systems, ISSN 0950-7051, e-ISSN 1872-7409
- Issue year
- 2019
- Vol
- 186
- Pages
- [1-19]
- Article number
- 104923
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 18-19; Bibliografia (liczba pozycji) - 66; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 186
- Keywords in English
- hexadecimal local pattern, multilevel DWT, ECG classification, pattern recognition, biomedical engineering
- DOI
- DOI:10.1016/j.knosys.2019.104923 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0950705119303727 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Publication indicators
- Citation count
- 192
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT124aa9cbf3294c218921e3cb9834d88a/
- URN
urn:pkr-prod:CUT124aa9cbf3294c218921e3cb9834d88a
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.