Abstract
The heart disease is one of the most serious health problems in today’s world. Over 50 million persons have cardiovascular diseases around the world. Our proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database (strongly imbalanced data) for one lead (modified lead II), from 29 people. In this work, we have used long-duration (10 s) ECG signal segments (13 times less classifications/analysis). The spectral power density was estimated based on Welch’s method and discrete Fourier transform to strengthen the characteristic ECG signal features. Our main contribution is the design of a novel three-layer (48 + 4 + 1) deep genetic ensemble of classifiers (DGEC). Developed method is a hybrid which combines the advantages of: (1) ensemble learning, (2) deep learning, and (3) evolutionary computation. Novel system was developed by the fusion of three normalization types, four Hamming window widths, four classifiers types, stratified tenfold cross-validation, genetic feature (frequency components) selection, layered learning, genetic optimization of classifiers parameters, and new genetic layered training (expert votes selection) to connect classifiers. The developed DGEC system achieved a recognition sensitivity of 94.62% (40 errors/744 classifications), accuracy = 99.37%, specificity = 99.66% with classification time of single sample = 0.8736 (s) in detecting 17 arrhythmia ECG classes. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
1 Introduction
In the last decades, solving problems from various fields, including medicine, using various machine learning (ML) techniques is very popular [1,2,3,4,5,6, 9, 46, 50, 53, 54, 57, 58, 60, 61, 75, 76].
This popularity is due to the fact that ML can cope with problems that are difficult to solve in a conventional way due to the unknown rules. Due to the properties of learning and generalization of knowledge, these methods are able to solve many problems. Artificial intelligence techniques achieve high performance in various fields of science. The advantages of ML (in particular, computational intelligence) lie in the properties inherited from their biological counterparts, such as learning and generalization of knowledge (e.g. artificial neural networks), global optimization (e.g. evolutionary algorithms), and use of imprecise terms (e.g. fuzzy systems).
The presented DGEC method draws inspiration from three areas of ML: (1) ensemble learning (EL), (2) deep learning (DL), and (3) evolutionary computation (EC). More information about EL and EC can be found in [51], and about DL in [77].
1.1 Heart diseases
Electrocardiography (ECG) is the most popular and the basic technique of diagnosing heart diseases. This is because it is noninvasive, simple and provides valuable information about the functioning of the circulatory system.
Cardiovascular diseases are serious social issue, because of: (a) highest mortality in the world (37% of all deaths, 17.3 million people per year [7, 8, 71]), (b) high incidence, and (c) high costs of treatment (long-lasting and expensive treatment caused by chronic course of the disease [34, 68]). The arguments quoted above will intensify because the number of deaths will grow from 17.3 million (2016) to 23.6 million (2030) [7, 8, 33, 71], which will be caused by the progressive aging of the population.
Current methods for the diagnosing of heart abnormalities are based on the calculation of the dynamic or morphological features of single QRS complexes. This solution is error-prone and difficult because of the variability of these characteristics in different persons [48]. For these reasons, the methods currently presented in the scientific literature do not obtain sufficient performance [23].
The above facts present a strong motivation to conduct research on new methods to support the medical diagnosis early and more effectively diagnose the heart disorders. An important aspect of our research is also to reduce the computational complexity of the developed algorithms in the context of implementing our solution in cloud computing or mobile devices to monitor the health of patients in real time.
1.2 Goals
The main goals of this paper are as follows:
-
Goal 1
Design new and efficient ensemble (network) of classifiers based on EL, DL, and EC for the automatic classification of cardiac arrhythmias based on segments of ECG signal.
-
Goal 2
Design method for patient self-control and prevention application in telemedicine and cloud computing or mobile devices characterized by low computational complexity.
-
Goal 3
Design universal method for the general population.
1.3 Novelty
Our main novel contribution is:
-
Deep genetic ensemble of classifiers (DGEC)
system consists of three-layer (48 + 4 + 1) ensemble (network) of classifiers made up of: 12 SVM (nu-SVC, RBF), 12 kNN, 12 PNN and 12 RBFNN classifiers + 4 SVM (C-SVC, linear) classifiers + 1 SVM (C-SVC, linear) classifier, based on: three normalization types, four Hamming window widths, four classifier types, stratified tenfold cross-validation (CV), genetic features (frequency components) selection, EL, DL, layered learning, genetic optimization of classifiers parameters, and new genetic layered training (expert votes selection) to connect classifiers.
Novel contributions of this work are based on works [12, 13, 22, 23], focused on:
-
N1
New deep multilayered structure of the system (ensemble of classifiers) provides appropriate information flow and fusion.
-
N2
New method of combining the system nodes (classifiers) based on genetic layered training (selection of classifiers/experts answers/votes).
1.4 Previous research
The work described in the article includes stage III of conducted research, and the remaining stages Nos. I and II are described in earlier articles:
-
Stage I
—the focus was on designing and testing methods for signal preprocessing, feature extraction, selection, and CV. In this type of work, only single classical classifiers were tested, optimized by genetic algorithm (GA). A description of this type of work is presented in the article [52].
-
Stage II
—the focus was on developing ML methods by designing, optimizing and testing a genetic (two layers, 18 or 11 classifiers) ensemble of classifiers, combining the advantages of EL and EC. In this research, only one type of classifier and one path of signal preprocessing and feature extraction were used. A description of this research is presented in the article [51].
-
Stage III
—the focus was on further developing ML methods by designing, optimizing and testing a more complex deep genetic (three layers, 53 classifiers) ensemble of classifiers combining the advantages of EL [21, 37, 38, 47, 62, 72], EC [14, 28] and DL [11, 16, 17, 27, 32, 39, 63]. In this research, four types of classifiers and 12 paths of signal preprocessing and feature extraction were used. This increase in diversity has increased the efficiency of the system. A description of this research is presented in this article.
The concept of ECG signal analysis presented in this article is based on previous research, but the solution proposed, in a decisive way, differs from previous works. The proper direction of development of the proposed idea resulted in obtaining definitely better results.
2 Materials and methods
2.1 Assumptions
The described study is based on the new methodology presented in articles [51, 52]. The main assumptions of the new methodology are as follows:
-
A1
Analysis 10-s segments of ECG signal (longer than single QRS complexes).
-
A2
No signal filtering
-
A3
No detection and segmentation of QRS complexes.
-
A4
Use of feature extraction comprising the estimation of the power spectral density.
-
A5
Use of feature (frequency components) selection based on GA.
-
A6
Analysis of ECG signal segments contains one type of class (with the exception of normal sinus rhythm) abnormality.
-
A7
Use of stratified tenfold CV method
-
A8
Use of the Winner-Takes-All (WTA) rule to classification of the ECG signal samples.
The use of the new approach has the following benefits: (1) reduced number of classifications (average 13 times, for a heart rate of 80 beats per minute) and (2) eliminating the detection and segmentation of QRS complexes. These advantages reduce the computational complexity, which enabled the use of the proposed solution in cloud computing or mobile devices (real-time processing). Also analyzing longer segments of ECG signal gives better outcomes for the classification of few diseases, for example, atrial-sinus and atrioventricular conduction blocks, Wolff–Parkinson–White syndrome, and elongates PQ intervals [52].
2.2 Materials
2.2.1 ECG dataset
The ECG signals were obtained from the MIT-BIH Arrhythmia [45] database from the PhysioNet [31] service. The details of the data used are given below.
-
The ECG signals were from 29 persons: 14 male (age: 32–89) and 15 female (age: 23–89).
-
The ECG signals contained 17 classes: normal sinus rhythm, 15 types of cardiac arrhythmias and pacemaker rhythm.
-
The ECG signals characteristics: (a) sampling frequency equal to 360 (HZ) and (b) gain equal to 200 (adu/mV).
-
744 ECG signal segments (10 s long, 3600 samples, without any overlapping) were randomly selected.
-
The ECG signals were derived from one lead (MLII).
-
The ECG signals contained at least ten segments for each recognized class.
Table 1 describes the details of the dataset used. It presents the analyzed ECG signal segments of cardiac dysfunctions: (a) the number of obtained for all dysfunction ECG segments, (b) the number of persons from whom the ECG segments were obtained, and (c) the division of ECG signal segments into training and testing sets for stratified tenfold CV.
The appropriate balance of data is an important aspect. In the research, a number of analyzed segments of ECG signal for all classes are in the range from 1.34% to 25.94%, imbalance ratio (IR) = 19, (see Table 1). More information about the dataset used can be found in the article [51].
2.3 Methods
In Fig. 1, subsequent phases of processing and analysis of the ECG signals are presented.
2.3.1 Phase I: preprocessing with normalization
Gain reduction and constant component reduction were used. Three types of normalization were tested: (a) standardization of signal (signal standard deviation = 1 and mean signal value = 0), (b) rescaling of signal to the range of \([-1,1] +\) reduction of constant component, and (c) lack of normalization. More information about signal preprocessing can be found in article [52].
2.3.2 Phase II: feature extraction
First, power spectral density (PSD) [64] of ECG signal was estimated based on Welsh method [70] and discrete Fourier transform (DFT) [64]. Subsequently, the transformed signal was logarithmized to normalize the frequency components of the PSD. Four Hamming window widths: (a) 128, (b) 256, (c) 512, and (d) 1024 samples, were used to calculate the PSD. More information about feature extraction can be found in articles [51, 52].
2.3.3 Phase III: feature selection
Three methods have been tested: (a) no selection, (b) selection based on GA [14, 59], and (c) selection based on particle swarm optimization (PSO [36]). The selection based on GA has obtained the best result and GA was applied for feature (frequency components) selection. Successive single ECG signal features (given as the inputs data of the classifiers) are represented as genes in the population of individuals. The following values can take genes: 0—rejected feature or 1—accepted feature. GA parameters are described in Tables 2 and 3.
2.3.4 Phase IV: cross-validation
Two methods have been tested: (a) stratified fourfold CV and (b) stratified tenfold CV [38]. Stratified tenfold CV method has obtained the best results. The testing and training sets were created by randomly selecting ECG signal segments separately for each class, maintaining the proportions between classes. Table 1 presents the selection of signal segments into training and testing sets. Stratified CV means that each set has proportional number of instances from all the classes. More information about the CV can be found in the article [51].
2.3.5 Phase V: machine learning algorithms
Nine methods have been tested: (a) support vector machine (SVM [19, 20], types: C-SVC, nu-SVC, nu-SVR, and epsilon-SVR), (b) probabilistic neural network (PNN, [67]), (c) k-nearest neighbor (kNN, [10]), (d) radial basis function neural network (RBFNN, [18]), (e) Takagi–Sugeno fuzzy system [69], (f) decision tree [56], (g) multilayer perceptron, (h) recurrent neural networks [55], and (i) discriminant analysis [44]. The results obtained due to the best performing classifiers, namely kNN, RBFNN, PNN, and SVM (nu-SVC and C-SVC) (Table 4), were presented. Parameters of the mentioned classifiers are presented in Tables 2 and 5.
2.3.6 Phase VI: parameter optimization
Three methods have been tested: (a) particle swarm optimization, (b) GA, and (c) grid search. Among them, GA has achieved the best results. Tables 2 and 3 describe the GA parameters used.
2.3.7 Evolutionary neural system
For single classifiers, the evolutionary neural systems comprising the SVM, kNN, PNN, and RBFNN classifiers have obtained the best results. The core of the evolutionary neural system is the classifier optimized using the GA. The selection of features and the optimization of the classifier parameters were carried out using a GA coupled with a stratified tenfold CV method.
2.3.8 Classical ensembles of classifiers (CEC)
A classic, two-layer bagging-type ensemble of classifiers were used. The first layer consisted of ten SVM classifiers (nu-SVC). SVMs are trained on the basis of the subsequent ten combinations of testing and training sets from stratified tenfold CV. In the second layer, the answers of classifiers from the first layer were combined using the majority voting method. The GA was used to optimize the CEC system.
2.4 Deep genetic ensemble of classifiers (DGEC)
DGEC is three-layer (48 + 4 + 1) system. In the DGEC system, each classifier of the first layer is optimized in order to maximize the accuracy of arrhythmia classification. In the second layer and third layer, based on the classifier answers from the first layer and based on DL and genetic selection of features, knowledge extraction process occurs leading to the final decision by the system.
In the previous work, we designed and tested: (1) genetic ensemble of classifiers optimized by classes (GECC system) and (2) genetic ensemble of classifiers optimized by sets (GECS system), described in [51].
2.4.1 Philosophy
The inspiration for the DGEC system was approach based on DL mimic nature on mechanisms occurring in the neocortex of the brain.
-
Characteristic features of system are as follows:
-
Classifiers as neurons, connected in network.
-
Layered learning—as in DL, the learning is progressing in stages.
-
Genetic layered training:
-
Optimization of connections between classifiers from adjacent layers (feature selection) realized by a GA is analogous to the elimination of connections in the brain between neurons.
-
Feedback occurring during training in the form of a GA (genetic optimization) and in the form of CV (training) is similar to back-connections in brain.
-
-
Diversity is present in classifiers, data preprocessing, and connections and is analogous to the different types of neurons, signal processing and irregular connections between neurons belonging to the neocortex of brain. The diversity of classifiers (four types) is included in the first layer. The diversity of data preprocessing is present in the first layer (three types of normalization and four types of Hamming window widths). The diversity of connections is between first and second layer and between second and third layer because do not occur all possible connections between the classifiers.
-
Bipolarity is noticeable by the value of transmitted signals from the set: \(\{0; 1\}\) similar to the value of the action potential of nerve cells (neurons).
-
Multilayered (depth)—according to the definition of DL, networks that have in their structure above two layers are considered as deep, which is analogous to the neocortex of brain, and it consists of seven layers.
-
Abstract learning is in the form of the internal extraction of features and transforming information in subsequent layers of its structure which generates more complex features that are abstract concepts, like in the brain.
-
-
The deep structure of designed system (network) consists of three layers, and the term genetic implies that in this research, GA plays a key role and ensemble of classifiers is comprised of 53 classifiers (nodes).
-
Layered learning—the first supervised training was performed for 48 classifiers from the first layer. The second supervised training was performed for four classifiers from the second layer based on the answers received from 48 models of classifiers from the first layer. The third supervised training was performed for one classifier from the third layer based on the answers received from four models of classifiers from the second layer.
-
Cross-validation—the stratified tenfold CV was coupled with GA (in first, second, and third layer of DGEC system), and all individuals (feature vectors) in the population were tested on all ten testing sets and ten training sets. This solution minimizes over-training.
-
First layer
-
Genetic feature selection was used to feature (frequency components) selection and parameter optimization for 48 classifiers in the first layer.
-
Optimization was done by GA (Table 2), and its purpose is parallel: (a) selection of ECG signal features and (b) optimization of classifier parameters (system nodes).
-
Votes (answers)—all classifiers (experts) have 17 outputs each with value of “0” or “1”. Value “1” occurs only in one output (indicated recognized, by the given classifier/expert, class, according to the WTA rule). Value “0” occurs on remaining 16 outputs (indicated not recognized classes).
-
-
Second and third layer
-
Genetic layered training was used to tune the ensemble of classifiers structure in second and third layer, relying on feature selection (experts or judges votes) from the first or second layer, based on reference answers. The aim of GA was to reject the incorrect answers (votes) of classifiers (nodes) from the first or second layer, based on the errors in all testing and training sets, and accept only correct answers (votes) as shown in Fig. 5. Genetic layered training is a novel approach of connecting classifiers (ensemble combination), and it is effective through transformation of one output into 17 outputs of classifiers.
-
2.4.2 First layer
In the first layer of the system are 48 experts comprising of 12 SVM (nu-SVC, RBF), 12 kNN, 12 PNN, and 12 RBFNN classifiers (three types of normalization and four Hamming window widths) optimized to minimize errors in recognition of ECG signal classes. ECG signal segments comprising the most characteristic features (chosen using GA) are given to the inputs of each of the 48 classifiers. For each of the 48 classifiers, the different and optimal set of parameter values has been selected, for SVM: \(\gamma (-g)\), \(\nu (-n)\), for kNN: exponent, for PNN: spread, and for RBFNN: spread.
2.4.3 Second layer
The second layer of the DGEC system has four judges with SVM classifiers (C-SVC, linear). These four meta-classifiers were developed in order to assess the experts votes from the first layer. Each of the judges was assigned to a group of 12 classifiers, which meant that the first judge evaluated the votes from 12 SVM classifiers, second from 12 kNN classifiers, third from 12 PNN classifiers, and fourth from 12 RBFNN classifiers. These 48 classifiers (from the first layer) have 17 outputs each, so the length of input features vector, to the second layer, is equal to 816 votes. Therefore, on the inputs of each of the four judges (classifiers from the second layer), 204 features are given.
2.4.4 Third layer
The third layer of the DGEC system has one judge, i.e., SVM classifier (C-SVC, linear). This one meta-classifier was designed to evaluate judges votes from the second layer. Four SVMs (from the second layer) have 17 outputs each, so the length of input features vector, to the third layer, is equal to 68 votes.
The analogous two-layer system (48 classifiers + 1 classifier) was also tested in the study. However, such a system was characterized by less effective training, which did not yield good ECG classification results. Therefore, this system is not widely discussed in the article. Hence, it can be concluded that another layer has contributed to boost the training efficiency of the system.
Many parameter configurations of GA and SVM, kNN, PNN, and RBFNN classifiers were tested as part of the study. The details of the optimization parameters are presented in Tables 2, 3, and 5.
Basic and optimizing parameters of 48 classifiers from the first layer of DGEC system and GA parameters used for feature selection and optimization of classifier parameters are presented in Table 2.
Basic and optimizing parameters of (4 + 1) classifiers in second and third layer of DGEC system and GA parameters used for feature selection and optimization of classifier parameters are presented in Table 3.
Figures 2 and 3 show the scheme of subsequent phases of information processing in DGEC system. In Fig. 4, scheme of connections between layers, information flow and fusion in DGEC system is presented. Algorithm 1 and 2 present the DGEC system algorithm. Figure 5 presents the scheme of genetic layered training (for single segment of ECG signal and exemplary chromosomes of individuals).
2.5 Evaluation criteria
The following coefficients have been determined for the evaluation of the designed methods [30, 65]: (1) sum of errors for 744 classifications (\(ERR_{sum}\)), (2) accuracy (ACC), (3) specificity (SPE), (4) sensitivity (SEN) = overall accuracy (Acc), (5) false positive rate (FPR), (6) positive predictive value (PPV), (7) \(\kappa\) coefficient, (8) time of optimization (\(T_o\)), (9) time of training (\(T_t\)), (10) time of classification (\(T_c\)), and (11) acceptance feature coefficient (\(C_F\)). Based on the confusion matrices generated, mentioned coefficients were calculated using values: TN—True Negative, TP—True Positive, FN—False Negative, FP—False Positive. More detailed information about the calculated coefficients can be found in the articles [51, 52].
The equations for the calculated coefficients are as follows:
-
Accuracy
$$\begin{aligned} ACC = \left. \left( \sum _{i=1}^N \frac{TP + TN}{TP + FP + TN + FN} \right) \cdot 100\% \bigg / \right. N \end{aligned}$$(2) -
Sensitivity = Overall Accuracy
$$\begin{aligned} SEN = Acc = \left. \left( \sum _{i=1}^N \frac{TP}{TP + FN} \right) \cdot 100\% \bigg / \right. N \end{aligned}$$(3) -
Specificity
$$\begin{aligned} SPE = \left. \left( \sum _{i=1}^N \frac{TN}{FP + TN} \right) \cdot 100\% \bigg / \right. N \end{aligned}$$(4) -
Positive Predictive Value
$$\begin{aligned} PPV = \left. \left( \sum _{i=1}^N \frac{TP}{TP + FP} \right) \cdot 100\% \bigg / \right. N \end{aligned}$$(5) -
False Positive Rate
$$\begin{aligned} FPR = \left. \left( \sum _{i=1}^N \frac{FP}{FP + TN} \right) \cdot 100\% \bigg / \right. N \end{aligned}$$(6) -
\({\kappa }\) coefficient (Fleiss’ kappa)
$$\begin{aligned} \kappa = \left. \left( \sum _{i=1}^N \frac{M \sum _{k=1}^n m_{k,k} - \sum _{k=1}^n (G_k C_k)}{M^2 - \sum _{k=1}^n (G_k C_k)} \right) \cdot 100\% \bigg / \right. N \end{aligned}$$(7)where N is number of sets applied in the stratified tenfold CV method = 10, k is index of class, n equal to 17 is a number of classes, M is total number of classified ECG signal segments that are compared to the reference responses (labels), \(m_{k,k}\) is the number of classified ECG signal segments belonging to the reference class k that have also been classified as a class j, \(C_k\) is total number of classified ECG signal segments belonging to class k, and \(G_k\) is total number of reference responses (labels) belonging to class k.
-
Acceptance feature coefficient (the smaller the better.)
$$\begin{aligned} C_F = \frac{F_a}{F} \cdot 100\% \end{aligned}$$(8)where \(F_a\) —the number of accepted features, and F—the number of all features. More information about evaluation criteria can be found in the articles [51, 52].
3 Results
The MATLAB R2014b software along with the LIBSVM library was used in the work. The computer with an Intel Core i7-6700K 4.0 GHz (only one core was used) with 32 GB of RAM for calculations was used. All calculation times, including optimization, training, and testing stages, are presented in Tables 4, 5, and 6, respectively.
We have achieved sensitivity (SEN) of 100% and \(ERR_ {sum}\) equal to 0 errors for all training sets.
A comparison of achieved outcomes, for the ensembles of classifiers: CEC, DGEC, and single classifiers: SVM, PNN, RBFNN, kNN, is presented in Table 4.
3.1 Deep genetic ensemble of classifiers
3.1.1 First layer
Table 5 presents detailed results, for the first layer of DGEC system for four classifiers (RBFNN, PNN, kNN, and SVM) with values of optimized parameters for: (a) three signal preprocessing types (no normalization, rescaling + constant component reduction, and standardization), (b) four feature extraction types (four Hamming window widths 1024, 512, 256, and 128 samples), and (c) one CV variant—stratified tenfold.
3.1.2 Second layer
Optimized parameter values and outcomes for the second layer (4 C-SVC linear, SVM classifiers) and third layer (1 C-SVC linear SVM classifier) of the DGEC system are presented in Table 6. \(ERR_{L}\) coefficient determines in all training sets sum of errors.
Figures 6, 7, 8, and 9 present the outcome visualizations of selection of features (genetic layered training), for the second layer of DGEC system. Accepted features (classifier answers) are shown by red dots. The votes (answers) of the 12 classifiers (experts) from the first layer were separated by a dashed black line. Figure 6 presents the answers of 12 SVM classifiers from the first layer, kNN in Fig. 7, PNN in Fig. 8, and RBFNN in Fig. 9.
3.1.3 Third layer
In Figs. 10, 11, 12, 14 and in Table 7, the results for entire DGEC system are presented.
In Fig. 10, the confusion matrix, for one SVM classifier from the third layer, is presented.
Figure 11 presents comparison of the following coefficients: sum of errors (ERR), accuracy (ACC), sensitivity (SEN), positive predictive value (PPV), specificity (SPE), false positive rate (FPR), and \(\kappa\) coefficient, for three variants of recognition (17, 15, and 12 classes).
In Fig. 12, percent error values for particular testing sets, from stratified tenfold CV method, for DGEC system, are presented.
Figure 13 presents the visualization of feature selection outcome (genetic layered training), for the third layer of DGEC system (one SVM classifier). Accepted features (answers of classifiers) are shown by red dots. The votes (answers) of the four SVMs (experts) from the second layer were separated by a dashed black line.
Table 7 indicates the detailed information on the efficiency of recognition for 17 ECG signal classes of the DGEC system. Coefficient \(ERR_{\%}\) indicates the percentage of errors.
In Fig. 14, ERR, ACC, SEN, PPV, SPE, and FPR for each class, are presented.
It can seen from Table 8 that we have obtained the highest classification performance using the same database for the classification of 17 ECG classes.
4 Discussion
4.1 Hypothesis
Achieved outcomes confirmed the hypothesis that the use of designed new DGEC system is effective, automatic, fast, computationally less complex and universal classification of myocardium dysfunctions using ECG signals.
Tables 4 and 8 show the results obtained by our novel method. Our proposed method obtained the \({SEN} = {94.62\%}\) (ACC = \(99.37\%\), SPE = \(99.66\%\)).
We have obtained the highest classification performance (Table 8). It should be emphasized that we have classified 17 classes, the classification sensitivity for 15 classes \(= 95\%\) and 12 classes = 98% (Fig. 11). Most of the other works from the literature present the results of the classification only for five classes.
In this work, we have used a single (10-s) ECG signal segment and the time required to test the ECG signal is only \({C_k}={0.8736}\) (s) for DGEC system. Hence, the developed system can be applied in telemedicine, cloud computing or mobile devices to aid the patients and clinicians to improve the accuracy of diagnosis.
4.2 Deep genetic ensemble of classifiers
Table 8 presents the results obtained using novel DGEC system, which confirm the validity of its design. The calculated coefficients for the DGEC system are: \({ERR_{sum}= {40}}\); \({SEN} = {94,62\%}\); \({\kappa } = {93,84\%}\). These values are better than the results achieved using the CEC system: \({ERR_{sum}} = {{75}}\); \({SEN} = {89,92\%}\); \({\kappa } = {88,38\%}\) and are better than the best single classifier—SVM: \({ERR_{sum}} = {{73}}\); \({SEN} = {90,19\%}\); \({\kappa } = {88,70\%}\).
Analyzing the outcomes from Tables 5 and 6, we understand that whole DGEC system achieved better result (\(EER_{sum} = 40\), \(SEN = 94,62\%\)) than the best component classifier from the first layer of DGEC system: \(EER_{sum} = 73\), \(SEN = 90.19\%\) or second layer of ensemble: \(EER_{sum} = 51\), \(SEN = 93.15\%\). For all component classifiers from the first layer, the average result is \(EER_{sum} = 96.25\), \(SEN = 87.06\%\).
It should be emphasized that the time of classification required for a single ECG signal segment (10-s) is only 0.8736 (s), although the whole ensemble is quite complex and consists of 53 classifiers.
4.3 Components of the classifier system
High performance of the DGEC system is presented in Tables 4, 5, 6, and Fig. 10. This result is obtained through: (1) appropriate connection of system nodes (classifiers) using genetic layered training in the second and third layer of the system, (2) diversity of the component classifiers (different classifiers make different errors) achieved by different signal normalization (three types), different Hamming window widths (four types), and different types of classifiers (four types), (3) adequate quality of the component classifiers of the system. Transformation of one output into 17 outputs of all component classifiers has enabled high performance of the genetic selection of votes (genetic layered training). Taking benefits from all component classifiers, and minimizing their drawbacks, was possible through combining classifiers using genetic feature selection.
4.4 Deep multilayer structure of the system
The success of designed DGEC system has been obtained based on: (1) genetic feature (frequency components) selection in the first layer of ensemble, (2) layered learning (accelerated and facilitated the training), (3) genetic layered training in the second and third layer (experts votes selection) applied to connection classifiers, (4) genetic parameters optimization (appropriate balance between exploitation and exploration) coupled with stratified tenfold CV, which significantly decreased the over-training and hence increased the performance of the DGEC method, and (5) DL (multilayer structure of system, in which extraction of features and pattern recognition occur through appropriate flow and fusion of information).
4.5 Deep learning
DGEC system based on DL. This section presents a comparison of the DGEC system with other DL algorithms such as the convolutional neural network (CNN).
-
Advantages of our system are given below:
-
obtained higher accuracy (e.g. compared to work [77]).
-
the possibility of greater interference in the optimization of the structure (selection of: nodes (classifiers), number of layers, connections between nodes, etc.).
-
-
Disadvantages of our method are given below:
-
complex structure requiring longer system design (longer training and optimization).
-
the feature extraction needs to be performed.
-
-
Similarities with other systems are as follows:
-
also a network of neurons (nodes process information), consisting of nodes in the form of classifiers.
-
also has a deep structure in which occur similar processes of fusion and flow information (with successive layers, the concepts are more and more abstract).
-
-
Differences with other state-of-art systems are as given below:
-
nodes, these are not classic neurons (with weights, biases, and activation functions) but more complex classifiers and each node is different (greater diversity of nodes).
-
outputs, instead of one there are 17 outputs from each node (classifier).
-
training and optimization, performed in stages, one by one in subsequent layers, and the results from the previous layer go to the next layer, in the CNN training and optimization is more global.
-
connections, flexibility in designing connections between nodes (classifiers) from different layers.
-
structure, in the first layer, nodes (classifiers) are called experts, and in the second and third layer are called judges. In the first layer, a processed ECG signal is given to the inputs of nodes, and in the second and third layer on the nodes (classifiers) inputs are given votes (17 answers with values of “0” or “1” indicated the recognized class) of each of the classifiers from the first layer.
-
structure tuning, eliminating incorrect voices (second and third layer), and ECG signal feature selection (first layer), and optimization of classifier (nodes) parameters is performed using GA.
-
4.6 Dysfunctions/classes
Classification performance for all classes of DGEC system is presented in Fig. 14. We can notice from the results that PPV of over 70% and SEN of over 70% are achieved despite using the imbalanced data, which is a significant success. The worst results have been achieved for fusion of ventricular and normal beats (\(PPV = 73.33\%\) and supraventricular tachyarrhythmia \(SEN = 72.73\%\)), which is shown in Table 7.
Heart abnormalities with the smallest values of PPV and SEN coefficients have been removed, based on the achieved results as shown in Fig. 14. Two other classification variants have been analyzed: 12 classes (after removing classes: SVTA, PVC, TRI, VT, and FUS, Table 1), and 15 classes (after removing classes: SVTA, and FUS, Table 1). The DGEC system achieved the SEN for 12, 15, and 17 classes 98.25%, 95.40%, and 94.62%, respectively, and \({\kappa } = \mathbf{97.92\% }\), 94.68%, and 93.84%, respectively.
5 Conclusion
The objective of this study was to develop new ML method, focusing on EC and also EL and DL approach which enables the effective classification of cardiac arrhythmias (17 classes: normal sinus rhythm, 15 types of arrhythmias and pacemaker rhythm) using ECG signal segments (10-s). Our main contribution is the design of a novel three-layer (48 + 4 + 1) genetic ensemble of classifiers. Novel system based on fusion of three normalization types, four Hamming window widths, four classifiers types, stratified tenfold CV, genetic feature (frequency components) selection, EL, layered learning, DL, EC, classifiers parameters optimization by GA, and new genetic layered training (expert votes selection) to connect the classifiers.
The DGEC system achieved a classification sensitivity of 17 cardiac arrhythmias (classes) equal to 94.62% (40 errors / 744 classifications, accuracy \(= 99.37\%\), specificity \(= 99.66\%\), classification time of single sample \(= \mathbf{0.8736 (s) }\)). To the best of our knowledge, this is the highest classification performance obtained for 17 ECG classes using 10 s ECG segments.
The salient features of our work are as follows: (1) strong imbalanced data for some classes, (2) classification of 17 classes of cardiac disorders, and (3) application of stratified tenfold CV method (analogous to subject-oriented validation scheme).
The authors have designed a new DGEC system for the effective (Table 8), automatic, fast (Table 4), low computational complexity (Sect. 1) and universal (Table 1), classification of cardiac disorders.
The strengths of the research are: (1) possibility to use our solution in telemedicine and implement designed method in mobile devices or cloud computing (only single lead, lower computational complexity, and low cost), (2) high performance, (3) classification of 17 heart disorders (classes), (4) design of new genetic layered training applied to connecting classifiers, and (5) design of novel ML method—DGEC system.
Due to the very promising results obtained, the described research is worth continuing. The next stages of research will include: (1) improving the accuracy of recognition myocardium dysfunctions through development and improving the algorithms based on fusion of EL and DL, (2) testing the other optimization methods based on EC, and (3) testing the efficiency of DGEC system with other physiologic signals.
Change history
15 July 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00521-024-10083-8
References
Abdar M (2015) Using decision trees in data mining for predicting factors influencing of heart disease. Carpathian J Electron Comput Eng 8(2):31–36
Abdar M, Yen NY, Hung JCS (2017) Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision trees. J Med Biol Eng. https://doi.org/10.1007/s40846-017-0360-z
Abdar M, Zomorodi-Moghadam M, Zhou X, Gururajan R, Tao X, Barua PD, Gururajan R (2018) A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognit Lett. http://www.sciencedirect.com/science/article/pii/S0167865518308766. Accessed 3 Dec 2018
Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 405:81–90. https://doi.org/10.1016/j.ins.2017.04.012
Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415–416:190–198. https://doi.org/10.1016/j.ins.2017.06.027
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396. https://doi.org/10.1016/j.compbiomed.2017.08.022
AHA (2003) International cardiovascular disease statistics. https://www.bellevuecollege.edu/artshum/materials/inter/Spring04/SizeMatters/InternatCardioDisSTATsp04.pdf. Accessed 19 June 2017
AHA (2016) Heart disease, stroke and research statistics at-a-glance. http://www.heart.org/idc/groups/ahamah-public/@wcm/@sop/@smd/documents/downloadable/ucm_480086.pdf. Accessed 19 June 2017
Alkeshuosh AH, Moghadam MZ, Mansoori IA, Abdar M (2017) Using PSO algorithm for producing best rules in diagnosis of heart disease. In: 2017 international conference on computer and applications (ICCA), pp 306–311. https://doi.org/10.1109/COMAPP.2017.8079784
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185
Arel I, Rose DC, Karnowski TP (2010) Deep machine learning—a new frontier in artificial intelligence research [research frontier]. IEEE Comput Intell Mag 5(4):13–18. https://doi.org/10.1109/MCI.2010.938364
Augustyniak P, Tadeusiewicz R (2007) Web-based architecture for ECG interpretation service providing automated and manual diagnostics. Biocybern Biomed Eng 27(1/2):233–241. https://doi.org/10.1.1.208.9247
Augustyniak P, Tadeusiewicz R (2009) Ubiquitous cardiology—emerging wireless telemedical application. In Augustyniak P, Tadeusiewicz R (eds) Chapter background 1: ECG interpretation: fundamentals of automatic analysis procedures, pp 11–71. IGI Global, Hershey. https://doi.org/10.4018/978-1-60566-080-6.ch002
Back T, Hammel U, Schwefel HP (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1(1):3–17. https://doi.org/10.1109/4235.585888
Bazi Y, Alajlan N, AlHichri H, Malek S (2013) Domain adaptation methods for ECG classification. In: 2013 international conference on computer medical applications (ICCMA), pp 1–4. https://doi.org/10.1109/ICCMA.2013.6506156
Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127. https://doi.org/10.1561/2200000006
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828. https://doi.org/10.1109/TPAMI.2013.50
Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Complex Syst 2:321–355
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018
Cruz RM, Sabourin R, Cavalcanti GD (2018) Dynamic classifier selection: recent advances and perspectives. Inf Fusion 41(Supplement C):195–216. https://doi.org/10.1016/j.inffus.2017.09.010
da Luz EJS, Nunes TM, de Albuquerque VHC, Papa JP, Menotti D (2013) ECG arrhythmia classification based on optimum-path forest. Expert Syst Appl 40(9):3561–3573. https://doi.org/10.1016/j.eswa.2012.12.063
da Luz EJS, Schwartz WR, Cámara-Chávez G, Menotti D (2016) ECG-based heartbeat classification for arrhythmia detection: a survey. Comput Methods Prog Biomed 127:144–164. https://doi.org/10.1016/j.cmpb.2015.12.008
de Chazal P, O’Dwyer M, Reilly RB (2004) Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 51(7):1196–1206. https://doi.org/10.1109/TBME.2004.827359
de Lammoy G, Francois D, Delbeke J, Verleysen M (2010) Weighted SVMs and feature relevance assessment in supervised heart beat classification. In: Fred A, Filipe J, Gamboa H (eds) Communications in computer and information science, chapter biomedical engineering systems and technologies, vol 127. Springer, Berlin, pp 212–223. https://doi.org/10.1007/978-3-642-18472-7
de Lannoy G, Francois D, Delbeke J, Verleysen M (2012) Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans Biomed Eng 59(1):241–247. https://doi.org/10.1109/TBME.2011.2171037
Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7:197–387. https://doi.org/10.1561/2000000039
Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002
Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Prog Biomed 127:52–63. https://doi.org/10.1016/j.cmpb.2015.12.024
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215
Goodfellow I, Bengio Y, Courville (2016) A deep learning. http://www.deeplearningbook.org. Book in preparation for MIT Press. Accessed 19 June 2017
Healthsquare (2007) Heart disease. In: Conference on computational intelligence for modelling control and automation, pp 179–182. http://www.healthsquare.com/heartdisease.htm. Accessed 19 June 2017
Heron MP, Smith BL (eds) (2003) Deaths: leading causes for 2003. National Center for Health Statistics, Hyattsville
Huang H, Liu J, Zhu Q, Wang R, Hu G (2014) A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals. Biomed Eng 13:1–26
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Krawczyk B, Minku LL, Gama J, Stefanowski J, Woźniak M (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37(Supplement C):132–156. https://doi.org/10.1016/j.inffus.2017.02.004
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New York
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
Lin CC, Yang CM (2014) Heartbeat classification using normalized RR intervals and wavelet features. In: 2014 international symposium on computer, consumer and control (IS3C), pp 650–653. https://doi.org/10.1109/IS3C.2014.175
Llamedo M, Martinez JP (2011) Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng 58(3):616–625. https://doi.org/10.1109/TBME.2010.2068048
Mar T, Zaunseder S, Martineznez JP, Llamedo M, Poll R (2011) Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 58(8):2168–2177. https://doi.org/10.1109/TBME.2011.2113395
Martis RJ, Acharya UR, Mandana K, Ray A, Chakraborty C (2012) Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 39(14):11792–11800. https://doi.org/10.1016/j.eswa.2012.04.072
Mclachlan GJ (2004) Discriminant analysis and statistical pattern recognition (Wiley series in probability and statistics). Wiley, New York. http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0471691151. Accessed 19 June 2017
Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50. https://doi.org/10.1109/51.932724
Naik GR, Selvan SE, Arjunan SP, Acharyya A, Kumar DK, Ramanujam A, Nguyen HT (2018) An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology. IEEE Trans Neural Syst Rehabil Eng 26(3):675–686. https://doi.org/10.1109/TNSRE.2018.2796070
Oza NC, Tumer K (2008) Classifier ensembles: select real-world applications. Inf Fusion 9(1):4–20. https://doi.org/10.1016/j.inffus.2007.07.002(Special Issue on Applications of Ensemble Methods)
Padmavathi K, Ramakrishna KS (2015) Classification of ECG signal during Atrial fibrillation using autoregressive modeling. Procedia Comput Sci 46:53–59. https://doi.org/10.1016/j.procs.2015.01.053. Proceedings of the International Conference on Information and Communication Technologies, ICICT 2014, 3–5 December 2014 at Bolgatty Palace& Island Resort, Kochi, India)
Park KS, Cho BH, Lee DH, Song SH, Lee JS, Chee YJ, Kim IY, Kim SI (2008) Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function. Comput Cardiol 2008:229–232. https://doi.org/10.1109/CIC.2008.4749019
Pławiak P (2014) An estimation of the state of consumption of a positive displacement pump based on dynamic pressure or vibrations using neural networks. Neurocomputing 144:471–483. https://doi.org/10.1016/j.neucom.2014.04.026
Pławiak P (2018) Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 39:192–208. https://doi.org/10.1016/j.swevo.2017.10.002
Pławiak P (2018) Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 92:334–349. https://doi.org/10.1016/j.eswa.2017.09.022
Pławiak P, Maziarz W (2014) Classification of tea specimens using novel hybrid artificial intelligence methods. Sensors Actuators B Chem 192:117–125. https://doi.org/10.1016/j.snb.2013.10.065
Pławiak P, Rzecki K (2015) Approximation of phenol concentration using computational intelligence methods based on signals from the metal-oxide sensor array. IEEE Sensors J 15(3):1770–1783. https://doi.org/10.1109/JSEN.2014.2366432
Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, Rojas I (2016) Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214:242–268. https://doi.org/10.1016/j.neucom.2016.06.014
Quinlan J (1986) Induction of decision trees. Mach Learn 1(1):81–106. https://doi.org/10.1023/A:1022643204877
Rajesh KN, Dhuli R (2017) Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput Biol Med 87:271–284. https://doi.org/10.1016/j.compbiomed.2017.06.006
Rajesh KN, Dhuli R (2018) Classification of imbalanced ECG beats using re-sampling techniques and adaboost ensemble classifier. Biomed Signal Process Control 41:242–254. https://doi.org/10.1016/j.bspc.2017.12.004
Rutkowski L (2008) Computational intelligence: methods and techniques. Springer, Berlin
Rzecki K, Pławiak P, Niedźwiecki M, Sośnicki T, Leśkow J, Ciesielski M (2017) Person recognition based on touch screen gestures using computational intelligence methods. Inf Sci 415–416:70–84. https://doi.org/10.1016/j.ins.2017.05.041
Rzecki K, Sośnicki T, Baran M, Niedźwiecki M, Król M, Łojewski T, Acharya U, Yildirim O, Pławiak P (2018) Application of computational intelligence methods for the automated identification of paper-ink samples based on libs. Sensors 18(11):3670. https://doi.org/10.3390/s18113670
Santos EMD, Sabourin R, Maupin P (2009) Overfitting cautious selection of classifier ensembles with genetic algorithms. Inf Fusion 10(2):150–162. https://doi.org/10.1016/j.inffus.2008.11.003
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003
Smith S (2002) Digital signal processing: a practical guide for engineers and scientists. Newnes, Oxford
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Soria ML, Martinez J (2009) Analysis of multidomain features for ECG classification. Comput Cardiol 2009:561–564
Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118
National Center for Health Statistics (ed.) (2005) Health, United States, 2005 with chartbook on the health of Americans. National Center for Health Statistics, Hyattsville
Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Pub. Co., Amsterdam
Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73. https://doi.org/10.1109/TAU.1967.1161901
WHO (2014) WHO global status report on noncommunicable diseases. http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1. Accessed 19 June 2017
Woźniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16(Supplement C):3–17. https://doi.org/10.1016/j.inffus.2013.04.006.(Special Issue on Information Fusion in Hybrid Intelligent Fusion Systems)
Yang W, Si Y, Wang D, Guo B (2018) Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine. Comput Biol Med 101:22–32. https://doi.org/10.1016/j.compbiomed.2018.08.003
Ye C, Kumar BVKV, Coimbra MT (2012) Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification. In: 2012 21st international conference on pattern recognition (ICPR), pp 2428–2431
Yildirim O (2018) A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med 96:189–202. https://doi.org/10.1016/j.compbiomed.2018.03.016
Yıldırım Ö, Baloglu UB, Acharya UR (2018) A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3889-z
Yildirim O, Pławiak P, Tan RS, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420. https://doi.org/10.1016/j.compbiomed.2018.09.009
Zhang Z, Dong J, Luo X, Choi KS, Wu X (2014) Heartbeat classification using disease-specific feature selection. Comput Biol Med 46:79–89. https://doi.org/10.1016/j.compbiomed.2013.11.019
Zhang Z, Luo X (2014) Heartbeat classification using decision level fusion. Biomed Eng Lett 4(4):388–395. https://doi.org/10.1007/s13534-014-0158-7
Zubair M, Kim J, Yoon C (2016) An automated ECG beat classification system using convolutional neural networks. In: 2016 6th international conference on IT convergence and security (ICITCS), pp 1–5. https://doi.org/10.1109/ICITCS.2016.7740310
Acknowledgements
The authors thanks Prof. R. Tadeusiewicz, Prof. Z. Tabor, and Prof. J. Leśkow for their valuable advice and guidance on the preparation of this article.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest in this work.
Additional information
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-024-10083-8
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Pławiak, P., Acharya, U.R. RETRACTED ARTICLE: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput & Applic 32, 11137–11161 (2020). https://doi.org/10.1007/s00521-018-03980-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-03980-2