Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system
Authors:
- Paweł Pławiak
Abstract
This article presents an innovative research methodology that enables the efficient classification of cardiac disorders (17 classes) based on ECG signal analysis and an evolutionary-neural system. From a social point of view, it is extremely important to prevent heart diseases, which are the most common cause of death worldwide. According to statistical data, 50 million people are at risk for cardiac diseases worldwide. The subject of ECG signal analysis is very popular. However, due to the great difficulty of the task undertaken, and high computational complexity of existing methods, there remains substantial work to perform. This research collected 1000 fragments of ECG signals from the MIH-BIH Arrhythmia database for one lead, MLII, from 45 patients. An original methodology that consisted of the analysis of longer (10-second) fragments of the ECG signal was used (an average of 13 times less classifications). To enhance the characteristic features of the ECG signal, the spectral power density was estimated (using Welch’s method and a discrete Fourier transform). Genetic optimization of parameters and genetic selection of features were tested. Pre-processing, normalization, feature extraction and selection, cross-validation and machine learning algorithms (SVM, kNN, PNN, and RBFNN) were used. The best evolutionary-neural system, based on the SVM classifier, obtained a recognition sensitivity of 17 myocardium dysfunctions at a level of 90.20% (98 errors per 1000 classifications, accuracy = 98.85%, specificity = 99.39%, time for classification of one sample = 0.0023 [s]). Against the background of the current scientific literature, these results are some of the best results to date.
- Record ID
- CUT1b36a3b80c0948b5833c7f2876144cbf
- Publication categories
- ;
- Author
- Journal series
- Expert Systems with Applications, ISSN 0957-4174, e-ISSN 1873-6793
- Issue year
- 2018
- Vol
- 92
- Pages
- 334-349
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 346-348; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 92
- Keywords in English
- ECG, biomedical signal processing and analysis, classification, machine learning algorithms, neural networks, support vector machine, k-nearest neighbor algorithm, evolutionary-neural system, genetic algorithm, feature extraction and selection, discrete fourier transform
- DOI
- DOI:10.1016/j.eswa.2017.09.022 Opening in a new tab
- URL
- http://www.sciencedirect.com/science/article/pii/S0957417417306292 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 35
- Publication indicators
- Citation count
- 254
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT1b36a3b80c0948b5833c7f2876144cbf/
- URN
urn:pkr-prod:CUT1b36a3b80c0948b5833c7f2876144cbf
* 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.