Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system
Introduction
Diagnosing heart conditions by analyzing ECG signals has been popular for many years and is the basic method used in the prevention of cardiovascular diseases. The wide range of application of ECG signal analysis is due to the fact that it is a simple and non-invasive method that provides substantial valuable information about the function of the circulatory system.
The huge popularity of the ECG signal analysis is also reflected in research. In recent years, the most developed topics related to electrocardiography include: 1) ECG beat detection / classification: (Augustyniak, 2015, Martis, Acharya, Adeli, 2014, Martis, Acharya, Min, 2013, Song, Cho, Kim, Lee, 2015, Yochum, Renaud, Jacquir, 2016), 2) deep learning: (Acharya, Fujita, Lih, Hagiwara, Tan, Adam, 2017, Kiranyaz, Ince, Gabbouj, 2016, Rahhal, Bazi, AlHichri, Alajlan, Melgani, Yager, 2016), 3) principal component analysis: (Castells, Laguna, Sörnmo, Bollmann, Roig, 2007, Ceylan, Ozbay, 2007, Chawla, 2009, Elhaj, Salim, Harris, Swee, Ahmed, 2016, Kallas, Francis, Honeine, Amoud, Richard, 2012, Kanaan, Merheb, Kallas, Francis, Amoud, Honeine, 2011, Kim, Shin, Shin, Lee, 2009, Martis, Acharya, Lim, Suri, 2013, Martis, Acharya, Mandana, Ray, Chakraborty, 2012, Martis, Acharya, Min, 2013, Polat, Gunes, 2007, Rodriguez, Mexicano, Bila, Cervantes, Ponce, 2015, Wang, Chiang, Hsu, Yang, 2013), 4) higher order statistics: (Martis, Acharya, Mandana, Ray, Chakraborty, 2013, Martis, Acharya, Prasad, Chua, Lim, Suri, 2013, Martis, Acharya, Ray, Chakraborty, 2011), 5) feature selection / dimensionality reduction: (Bereta, Burczyński, 2007, Doquire, de Lannoy, Francois, Verleysen, 2011, Doquire, de Lannoy, Francois, Verleysen, 2011, Kishore, Singh, 2015, Lin, Ying, Chen, Lee, 2008b, Llamedo, Martinez, 2011, Mar, Zaunseder, Martineznez, Llamedo, Poll, 2011, Martis, Acharya, Adeli, Prasad, Tan, Chua, et al., 2014, Nasiri, Naghibzadeh, Yazdi, Naghibzadeh, 2009, Oh, Lee, Moon, 2004, Wang, Yang, Teng, Xia, Jensen, 2007, Yeh, Wang, Chiou, 2010, Yu, Lee, 2012, Zhang, Dong, Luo, Choi, Wu, 2014), 6) noise: (Li, Rajagopalan, Clifford, 2014, Pasolli, Melgani, 2015, Roonizi, Sassi, 2016), 7) discrete wavelet transform: (Augustyniak, 2003; Daamouche, Hamami, Alajlan, Melgani, 2012, Elhaj, Salim, Harris, Swee, Ahmed, 2016, Guler, Ubeyli, 2005, Islam, Haque, Tangim, Ahammad, Khondokar, 2012, Kutlu, Kuntalp, 2012, Lin, Du, Chen, 2008a, Martis, Acharya, Min, 2013, Mishra, Thakkar, Modi, Kher, 2012, Thomas, Das, Ari, 2015, Yan, Lu, 2014), 8) independent component analysis: (Chawla, 2009, Elhaj, Salim, Harris, Swee, Ahmed, 2016, Martis, Acharya, Min, 2013, Sarfraz, Khan, Li, 2014, Yu, Chou, 2008, Yu, Chou, 2009), 9) ensemble learning: (Guler, Ubeyli, 2005, Huang, Liu, Zhu, Wang, Hu, 2014, Javadi, Arani, Sajedin, Ebrahimpour, 2013, Mert, Kılıç, Akan, 2012, Osowski, Hoai, Markiewicz, 2004, Osowski, Markiewicz, Hoai, 2008, Sambhu, Umesh, 2013), 10) hybrid systems: (Engin, 2004, Meau, Ibrahim, Narainasamy, Omar, 2006, Osowski, Linh, 2001, Osowski, Markiewicz, Hoai, 2008, Ozbay, Ceylan, Karlik, 2006).
Currently, we observe a very high incidence of cardiovascular disease and the very high mortality caused by them. Despite the preventive measures taken, cardiovascular diseases are the leading cause of death worldwide (17.3 million people per year, accounting for 37% of all deaths (AHA, 2004, AHA, 2016, WHO, 2014)) and the most serious and costly health problems facing the world today (Heron, Smith, 2003, National Center for Health Statistics, 2005). Circulatory system diseases are usually chronic diseases that require long-term and expensive treatment. The tendency for the incidence of cardiovascular diseases will increasingly intensify due to the progressive aging of the population (the number of deaths will increase from 17.3 million in 2016 to 23.6 million in 2030 (AHA, 2004, AHA, 2016, Healthsquare, 2007, WHO, 2014)).
The classification of cardiac disorders based on existing methods based on the calculation of morphological and dynamic features of individual QRS complexes (heart evolution) is difficult and error prone due to the variability of these features in different patients (Padmavathi & Ramakrishna, 2015). For this reason, solutions currently described in the scientific literature do not achieve a satisfactory efficiency (da S. Luz, Schwartz, Cmara-Chvez, & Menotti, 2016).
The existing approaches are also ineffective for certain cardiac disorders, characterized by complex dependencies between subsequent evolutions of heart, for which the most important are “prolapsed” evolutions of heart (time intervals between subsequent heartbeats) and not the QRS complexes that may be correct. The group of these dysfunctions can include pre-excitation syndromes (e.g., Wolff-Parkinson-White syndrome - WPW), atrio-ventricular and atrial-sinus conduction blocks, and elongate PQ intervals.
This is why it is very important to develop specialized software supporting medical diagnostics to more effectively identify heart pathologies earlier and monitor the conditions of patients in real time. The reduction in computational complexity is also an important aspect in the context of deploying the solution in mobile devices.
For recent years, we can distinguish two main approaches in the literature on the automatic recognition of cardiac disorders based on the analysis of ECG signals:
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classification of QRS complexes (Alvarado, Lakshminarayan, Principe, 2012, de Chazal, O’Dwyer, Reilly, 2004, Mateo, Torres, Aparicio, Santos, 2016, Oster, Behar, Sayadi, Nemati, Johnson, Clifford, 2015, Ye, Kumar, Coimbra, 2012a, Zhang, Luo, 2014),
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analysis of longer ECG signal fragments (Abawajy, Kelarev, Chowdhury, 2013, Padmavathi, Ramakrishna, 2015, Romero, Serrano, 2001, Vafaie, Ataei, Koofigar, 2014).
It should be noted that the first approach concerning the classification of QRS complexes is substantially more popular. A key element of this approach is the effective detection of QRS complexes. On this basis, it is possible to segment an entire signal into individual QRS complexes and then analyze them using morphological features (determining the shape of the heart evolutions) and dynamic features (determining dependencies between subsequent heart evolutions).
An alternative approach is the analysis of longer, from a single QRS complex (lasting approximately 1 s), signal fragments, usually lasting approximately 10 s; this is the time period corresponding to a standard ECG examination at a cardiologist. Such analysis is based on distinctive feature extraction, for a given disorder, for whole, longer fragments. The identification of heart pathology is based on the extracted features.
Based on a current literature review (Augustyniak, & Tadeusiewicz, da S. Luz, Nunes, de Albuquerque, Papa, Menotti, 2013, da S. Luz, Schwartz, Cmara-Chvez, Menotti, 2016), the typical research methodology in the field of ECG signal analysis consists of
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obtaining data from public databases (MIT-BIH, EDB, AHA, CU, and NST),
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pre-processing and signal normalization,
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QRS detection and ECG signal segmentation,
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extraction of characteristic signal features and rejection of redundant and erroneous information (extraction and selection of features),
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classification of QRS complexes (recognition of heart disorders), e.g., data cross-validation, training, testing and optimization of classifier parameters, and
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evaluation of the obtained results.
In the literature, the most popular method for creating training and test sets is cross-validation, where the two most popular validation schemes are Afkhami, Azarnia, and Tinati (2016) and da S. Luz et al. (2016)
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class-oriented validation schemes (intra-patient paradigm) - the selection of elements for training and test sets based on signals from the same patient, and
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subject-oriented validation schemes (inter-patient paradigm) (de Chazal et al., 2004) - the selection of elements for training and test sets based on signals from other patients.
Designing universal algorithms for the general population, not for an individual person, using a subject-oriented validation scheme is a better solution. This solution demonstrates lower effectiveness on the test set but is more reliable and stable and performs better in practice due to the smaller fit of the models to the training set and better knowledge generalization (Afkhami et al., 2016).
The evolutionary-neural system (Rutkowski, 2008) is a hybrid that combines the advantages of two computational intelligence methods: broadly defined Neural Networks (Prieto et al., 2016) and Evolutionary Computation (Back, Hammel, & Schwefel, 1997). With this synergy, we can achieve greater efficiency through better optimization of the classifier tuning by Genetic Algorithm (Holland, 1992) that are parts of the system. In the field of heart disorders recognition, evolutionary-neural systems are also popular and used with success: (Daamouche, Hamami, Alajlan, Melgani, 2012, Dilmac, Korurek, 2015, Ince, Kiranyaz, Gabbouj, 2009, Khazaee, Ebrahimzadeh, 2010, Korurek, Dogan, 2010, Lessmann, Stahlbock, Crone, 2006, Melgani, Bazi, 2008, Shadmand, Mashoufi, 2016).
The main aims of the research were the following:
Aim 1 Develop new and effective methods for the automatic recognition of myocardium dysfunctions based on ECG signals modeled on the work of cardiologists.
Aim 2 Design algorithms for use in tele-medicine and mobile devices for patient self-control and prevention applications (low computational complexity).
Aim 3 Design universal algorithms not for individuals but for the general population.
Based on a literature review (da S. Luz, Nunes, de Albuquerque, Papa, Menotti, 2013, da S. Luz, Schwartz, Cmara-Chvez, Menotti, 2016), it can be stated that the innovative elements of this research include the following:
Methodology - a new approach to ECG signal analysis. The designed system is modeled on the work of a cardiologist based on the analysis of longer (10-s) ECG signal fragments, which contain multiple heart evolutions.
17 recognized classes - normal sinus rhythm + pacemaker rhythm + 15 cardiac disorders.
Genetic training and optimization of classifiers - a genetic algorithm coupled with 10-fold cross-validation for signal feature selection and classifier parameter optimization.
The innovative elements of this research in the analysis of long (10-s) fragments of ECG signals include the following:
Feature extraction - strengthen the characteristic features of signals by estimating the power spectral density using the Welch method and the discrete Fourier transform (data analysis in the frequency domain for several Hamming window widths).
Genetic selection of features – the elimination of redundant features (frequency components of the power spectral density of the ECG signal) by a genetic algorithm.
Section snippets
ECG database
For research purposes, the ECG signals were obtained from the http://www.physionet.org PhysioNet (Goldberger et al., 2000) service from the MIT-BIH Arrhythmia (Moody & Mark, 2001) database. The created database with ECG signals is described below.
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The ECG signals were from 45 patients.
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The ECG signals contained 17 classes: normal sinus rhythm, pacemaker rhythm, and 15 types of cardiac dysfunctions (for each of which at least 10 signal fragments were collected).
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All ECG signals were recorded at a
Results
All combinations of methods (2.4) have been tested on a smaller database containing 744 ECG fragments and 29 patients. This article presents the results for the 8 paths that have achieved the highest sensitivity (SEN): 1 type of normalization x 2 Hamming window widths x 4 types of classifiers. The results obtained were very similar for both variants of databases (744 and 1000 ECG fragments).
The study utilized the MATLAB R2014b environment together with the LIBSVM library (Chang & Lin, 2011).
Hypothesis
The results obtained in all experiments confirmed the thesis: the application of the proposed methodology will enable the automatic, efficient, universal, low computational complexity and fast recognition of heart disorders based on ECG signal analysis and the evolutionary-neural system.
The confirmation of this statement is given by the obtained results, summarized in Table 3, Table 4, Table 5 and in 4.11 Computational complexity, 4.9 Times. The presented results show that the recognition
Conclusion
The aim of the conducted research was to develop a new methodology that enables the efficient recognition of myocardium dysfunctions (17 classes: normal sinus rhythm + pacemaker rhythm + 15 heart disorders), based on analysis of 10-s fragments of ECG signals and an evolutionary-neural system. In this research, 1000 fragments of ECG signals were analyzed from the MIH-BIH Arrhythmia database for one lead, MLII, from 45 patients. Four experiments were conducted, during which many methods were
Acknowledgments
We thanks Professor R. Tadeusiewicz and Professor Z. Tabor for their valuable advice and guidance on the preparation of this paper.
P. Pławiak was born in Ostrowiec, Poland, in 1984. He obtained his M.Sc. degree in Electronics and Telecommunications and his Ph.D degree with honors in Biocybernetics and Biomedical Engineering at the AGH University of Science and Technology, Cracow, Poland, in 2012 and 2016, respectively. He is an Assistant Professor with the Institute of Telecomputing, Cracow University of Technology, Cracow, Poland. His research interests include machine learning algorithms and computational intelligence
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P. Pławiak was born in Ostrowiec, Poland, in 1984. He obtained his M.Sc. degree in Electronics and Telecommunications and his Ph.D degree with honors in Biocybernetics and Biomedical Engineering at the AGH University of Science and Technology, Cracow, Poland, in 2012 and 2016, respectively. He is an Assistant Professor with the Institute of Telecomputing, Cracow University of Technology, Cracow, Poland. His research interests include machine learning algorithms and computational intelligence methods (e.g., artificial neural networks, genetic algorithms, fuzzy systems, support vector machines, k-nearest neighbors, and hybrid systems), pattern recognition, signal processing and analysis, data analysis and data mining, sensor techniques, medicine, biocybernetics, and biomedical engineering.