Types Of Heartbeats Research Articles

A novel method for ECG signal classification via one-dimensional convolutional neural network

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods.

Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods

Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy decision function. This classification detects the existence of the arrhythmia and it helps the physicians to treat the heart patient with more accurate way. To train SVM, the MIT BIH Arrhythmia database is used which works with the heart disorder like sinus bradycardy, old inferior myocardial infarction, coronary artery disease, right bundle branch block. All three methods are implemented in proper way, and their rate of accuracy with SVM classifier is optimal when it is processed with the one-against-all method. The data sets of ECG arrhythmia are usually complex in nature, so for the SVM based classification one-against-all method has great impact and will fetch better result.

Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification

Automatic heartbeat classification technology based on the ECG plays an important role in assisting doctors with arrhythmia diagnosis. While many heartbeat classification studies can achieve good performance under the intra-patient paradigm, they still cannot offer acceptable classification results under the inter-patient paradigm. Additionally, the available ECG datasets are highly class imbalanced since normal heartbeats appear much more frequently than abnormal heartbeats, resulting in most methods having low sensitives and positive predictive values on minority class ectopic heartbeats. To solve the above problems, this study proposes an automatic ECG heartbeat classification method based on ensemble learning and multi-kernel learning. First, we use a linear combination of the radial basis function kernel and the polynomial kernel to produce a mixed-kernel-based extreme learning machine (MKELM). Then, a MKELM-based random forest binary classifier (MKELM-RF) is constructed. Finally, an ensemble multiclass classifier MKELM-RF-OVO is proposed based on one-vs.-one (OVO) reduction and MKELM-RF. We evaluated the proposed method on the public MIT-BIH-AR benchmarks database, under the inter-patient paradigm, classifying four types of heartbeats, namely, normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V) and the fusion of ventricular and normal (F). The obtained overall accuracy and the average positive predictive value are 98.1% and 93.9%, respectively, which are higher than the current studies by approximately 4% and 6%, respectively. The sensitivities for classes S and V are 1 and 94.4%, respectively, which outperforms most methods. The evaluation results show that our proposed method achieves a superior classification performance compared to the state-of-the-art methods.

Adversarial Multi-Task Learning for Robust End-to-End ECG-based Heartbeat Classification.

In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-consuming process. Furthermore, this process is error-prone due to noise from the recording equipment and biological non-idealities of patients. Thus, an automated arrhythmia classifier would be time and cost-effective as well as offer better generalization across patients. In this paper, we propose an adversarial multitask learning method to improve the generalization of heartbeat arrythmia classification. We built an end-to-end deep neural network (DNN) system consisting of three sub-networks; a generator, a heartbeat-type discriminator, and a subject (or patient) discriminator. Each of these two discriminators had its own loss function to control its impact. The generator was "friendly" to the heartbeat-type discrimination task by minimizing its loss function and "hostile" to the subject discrimination task by maximizing its loss function. The network was trained using raw ECG signals to discriminate between five types of heartbeats - normal heartbeats, right bundle branch blocks (RBBB), premature ventricular contractions (PVC), paced beats (PB) and fusion of ventricular and normal beats (FVN). The method was tested with the MIT-BIH arrhythmia dataset and achieved a 17% reduction in classification error compared to a baseline using a fully-connected DNN classifier.Clinical Relevance-This work validates that it is possible to develop a subject-independent automated heart arrhythmia detection system to assist clinicians in the diagnosis process.

Deep Multi-instance Networks for Bundle Branch Block Detection from Multi-lead ECG.

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat: right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.

Generating electrocardiogram signals by deep learning

Given the importance of a diverse and vast amount of realistic and labeled electrocardiogram (ECG) signals in improving the performance of biomedical signal processing algorithms, and the situation of severe lack of the signals, three generative models based on deep learning are introduced for the generation of ECG signals: The WaveNet-based model, the SpectroGAN model, and the WaveletGAN model. The WaveNet-based model adopts μ-law companding transformation as a preprocessing method and then is followed by a sequence of convolutional layers with dilation; SpectroGAN and WaveletGAN use short-term Fourier transform (STFT) and stationary wavelet transform (SWT) respectively to obtain suitable input form for the generative adversarial networks (GAN). Our proposed models are capable of generating ECG signals containing three different heartbeat types: normal beat, left bundle branch block beat and right bundle branch block beat. The synthetic ECG signals generated by our models are more realistic since deep artificial neural networks can discover intricate structure and characteristics of real ECG signals instead of manually setting specific parameters for synthesis. Besides, ECG signals produced by one of our proposed models could be naturally continuous and be up to more than 20 seconds. Furthermore, we first provide an evaluation approach for quantitatively demonstrating the performance of ECG generative models. The study demonstrates that deep learning is a feasible and effective method for ECG generation. Our proposed ECG generative models can be utilized to assess biomedical signal processing algorithms so as to improve their performance in clinical trials.

Optimized time–frequency features and semi-supervised SVM to heartbeat classification

One of the most significant indicator of heart disease is arrhythmia showing heartbeat patterns. Thus, early and accurate detection of arrythmia types by categorization of heartbeats is important. In this paper, we introduce an ECG beat classifier system integrating two main parts: feature extraction and classification. For the first part, we consider the features observed in the time–frequency (t, f) plane where the ECG is projected using a variant of Stockwell transform. For the second part, the framework of semi-supervised SVM with asymmetric costs (AS3VM) has been applied for assessment of the obtained feature sets performance. Notice that four heartbeat types have been considered: normal beats (N), left and right bundle branch blocks (L and R) and premature ventricular contractions (V). The proposed method has been evaluated on PhysionNet’s MIT-BIT arrythmia database. The obtained results show that the suggested approach achieves significant separability of the classes and thus, able to make prediction accuracies of $$99.35\%$$ , $$98.73\%$$ , $$98.57\%$$ and $$99.44\%$$ for, respectively, N, L, R and V beats.

A framework for cardiac arrhythmia detection from IoT-based ECGs

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework.

Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology

ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types - normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance.

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram

BackgroundThe electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasive nature. Arrhythmia can be classified into many types, including life-threatening and non-life-threatening. Accurate detection of arrhythmic types can effectively prevent heart disease and reduce mortality. MethodsIn this study, a novel deep learning method for classification of cardiac arrhythmia according to deep residual network (ResNet) is presented. We developed a 31-layer one-dimensional (1D) residual convolutional neural network. The algorithm includes four residual blocks, each of which consists of three 1D convolution layers, three batch normalization (BP) layers, three rectified linear unit (ReLU) layers, and an “identity shortcut connections” structure. In addition, we propose to use 2-lead ECG signals in combination with deep learning methods to automatically identify five different types of heartbeats. ResultsWe have obtained an average accuracy, sensitivity and positive predictivity of 99.06%, 93.21% and 96.76% respectively for single-lead ECG heartbeats. In the 2-lead datasets, the results show that the deep ResNet model has high classification performance, achieving an accuracy of 99.38%, sensitivity of 94.54%, and specificity of 98.14%. ConclusionThe proposed method can be used as an adjunct tool to assist clinicians in their diagnosis.

R-Peak Detection Using Chaos Analysis in Standard and Real Time ECG Databases

ObjectivesTimely and accurate R-peak detection is very important for analyzing electrocardiogram (ECG) signal in critical conditions. The main obstacle in observing the correct relation between underlying physiology and features is that there is no specific method to select the features that are needed for diagnosis of a particular heart disease. Therefore, choice of an advanced feature extraction technique is a major concern especially due to non-linear nature of the ECG signal. Material and methodsIn this study, physioNet (standard) and real-time ECG records have been used. During recording, ECG signal is affected by various noises/interferences which create further challenges in ECG signal analysis. Hence, it requires an effective pre-processing, advanced feature extraction and detection techniques. In this paper, independent principal component analysis (IPCA) is used for pre-processing, since it possesses good characteristic of both principal component analysis (PCA) and independent component analysis (ICA). Due to non-linear nature of ECG signals, chaos analysis is applied in feature extraction stage for different ECG databases. The monitoring and wide description of chaotic patterns of heartbeats are prime concerns for cardiologists. Chaos analysis has been used by estimating different attractors against various time delay dimensions. Correct R-peak detection is useful in diagnosing cardiac diseases and performance of the proposed methodology has been evaluated in terms of sensitivity (Se), positive predictivity (PP), and detection error rate (DER) for both PhysioNet (PN DB) and real-time (RT DB) databases. Results-case-I: Without pre-processingIn this case, R-peaks have been detected using chaos analysis+PCA. The proposed method yields Se of 99.91%, PP of 99.93%, and DER of 0.163% for PN DB and Se of 99.77%, PP of 99.83%, and DER of 0.387% for RT DB. Case-II: With pre-processingIn this case, R-peaks have been detected using IPCA+chaos analysis+PCA. The proposed method yields Se of 99.95%, PP of 99.96%, and DER of 0.093% for PN DB and Se of 99.96%, PP of 99.97%, and DER of 0.055% for RT DB. ConclusionThe proposed technique outperforms the other existing works on various selected evaluation parameters even without pre-processing. Hence, the proposed technique has successfully demonstrated its ability to discriminate different types of heartbeats in most of the critical situations. Therefore, there are strong merits in using chaos analysis as a feature extraction method to reduce the incidence of false diagnosis.

A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet.

Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are extensively employed, however, most such algorithms can only utilize one-lead ECGs. Hence, the potential information in other-lead ECGs was not utilized. To address this issue, we have developed novel methods for diagnosing arrhythmia. In this work, DL-CCANet and TL-CCANet are proposed to extract abstract discriminating features from dual-lead and three-lead ECGs, respectively. Then, the linear support vector machine specializing in high-dimensional features is used as the classifier model. On the MIT-BIH database, a 95.2% overall accuracy is obtained by detecting 15 types of heartbeats using DL-CCANet. On the INCART database, overall accuracies of 94.01% (II and V1 leads), 93.90% (V1 and V5 leads) and 94.07% (II and V5 leads) are achieved by detecting seven types of heartbeat using DL-CCANet, while TL-CCANet yields a higher overall accuracy of 95.52% using the above three leads. In addition, all of the above experiments are implemented using noisy ECG data. The proposed methods have potential to be applied in the clinic and mobile devices.

Application of Dispersion Entropy to Healthy and Pathological Heartbeat ECG Segments.

Entropy quantification algorithms are a prominent tool for the quantification of irregularity in biological signal segments towards the characterization of the physiological state of individuals. This paper investigates the potential of Dispersion Entropy (DisEn) as a non-linear method to quantify the uncertainty of ECG signal segments for different types of heartbeats and the stratification of healthy heartbeats for the potential detection of developing pathologies in individuals. Our results indicate that the DisEn algorithm produces distributions with significant differences for the considered types of heartbeats, with higher DisEn values being more prominent in pathological heartbeats and normal heartbeats preceding them. This suggests that, with further research, DisEn algorithms can be integrated with heartbeat detection and classification algorithms for the improvement of medical prognosis through ECG signal processing.

Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat. After removing the noise from ECG signals by a low-pass filter, the two-lead heartbeat segments with 2-s length were generated on the filtered signals, and classified by an adaptive ResNet model. The proposed method was evaluated on the MIT-BIH Arrhythmia Database with the patient-specific pattern. The overall accuracy was 98.6% and sensitivity of N, S, V, F were 99.4%, 85.4%, 96.6%, 90.6% respectively. Experimental results show that the proposed method achieved a good performance, and would be useful in the clinic practice.

NI LabVIEW Biyomedikal Yazılımı ile EKG İşaretlerinin Öznitelik Çıkarımı ve Yapay Sinir Ağları ile Sınıflandırılması

In this study, a data set containing normal and different heart beat types recorded by the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) was used for the detection of cardiac dysfunctions. In this data set, features were extracted using the LabVIEW Biomedical Workbench from the normal heartbeat and six different arrhythmia types. Obtained signals were evaluated by using Artificial Neural Network multiple classification method. Classification performances were compared before extracting the feature on the same data set. Classifier performances were evaluated by accuracy, sensitivity and selectivity performances criteria of classification. In the classifier performances, the "Normal" beat rate was found to be 99% accurate with the highest success compared to other arrhythmia types. As a result, both analysis methods are successful, but when the LabVIEW Biomedical Workbench is used, the classification results have achieved higher success.

Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters

Abstract Electrocardiogram (ECG) is commonly used to analyze heartbeats. Arrhythmia is a disturbance of the heartbeat that can potentially be lethal. Arrhythmia can be detected by identifying an individual abnormal heartbeat, which can occur in isolation or sequentially. Heartbeats can be classified into five types: non-ectopic (N), supra ventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beats (Q). Automated classification of the incidence and pattern of abnormal heartbeats enables arrhythmia detection, triage and life-threatening arrhythmia. Therefore, it is vital to develop a tool for the classification of these classes. ECG signals are contaminated by noise rendering difficulty and challenging to discriminate between different types of heartbeats. In this work, we used noisy as well as denoised (clean) ECG signals to classify heartbeats. Using stop-band energy (SBE) minimized dyadic orthogonal filter bank, wavelet decomposition of the ECG signals was performed. After this, fuzzy entropy, Renyi entropy and fractal dimension features were extracted for easier and accurate classification into the five classes. These features were then fed to classifiers, achieving maximum accuracy (MAAC) of 98%, maximum sensitivity (MASE) of 85.33% and maximum specificity (MASP) of 98.22% for noisy data, and MAAC of 98.1%, MASE of 85.63% and MASP of 98.27% for clean data, with KNN classifier. Our developed system can be used in intensive care units to assist the clinicians to aid in their diagnosis.

A Novel Wearable Electrocardiogram Classification System Using Convolutional Neural Networks and Active Learning

Arrhythmias reflect electrical abnormalities of the heart, and they can lead to severe harm to the heart. An electrocardiogram (ECG) is a useful tool to manifest arrhythmias. In this paper, we present an automatic system using a convolutional neural network and active learning to classify ECG signals. To improve the model performance, breaking-ties (BT) and modified BT algorithms are utilized in the active learning. We classify ECG signals in five heartbeat types, i.e., normal (N), ventricular (V), supraventricular (S), fusion of normal and ventricular (F), and unknown heartbeats (Q), using the Association for the Advancement of Medical Instrumentation standard. Our experiments are performed on the MIT-BIH arrhythmia database. To further verify the generalization capability of the system, the ECG data that acquired from our wearable device are also used to conduct in the experiments. Compared with most of the state-of-the-art methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably.

ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network

The classification of electrocardiogram (ECG) signals is very important for the automatic diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of feature extraction and the step of pattern classification. Owing to recent advances in artificial intelligence, it has been demonstrated that deep neural network, which trained on a huge amount of data, can carry out the task of feature extraction directly from the data and recognize cardiac arrhythmias better than professional cardiologists. This paper proposes an ECG arrhythmia classification method using two-dimensional (2D) deep convolutional neural network (CNN). The time domain signals of ECG, belonging to five heart beat types including normal beat (NOR), left bundle branch block beat (LBB), right bundle branch block beat (RBB), premature ventricular contraction beat (PVC), and atrial premature contraction beat (APC), were first transformed into time-frequency spectrograms by short-time Fourier transform. Subsequently, the spectrograms of the five arrhythmia types were utilized as input to the 2D-CNN such that the ECG arrhythmia types were identified and classified finally. Using ECG recordings from the MIT-BIH arrhythmia database as the training and testing data, the classification results show that the proposed 2D-CNN model can reach an averaged accuracy of 99.00%. On the other hand, in order to achieve optimal classification performances, the model parameter optimization was investigated. It was found when the learning rate is 0.001 and the batch size parameter is 2500, the classifier achieved the highest accuracy and the lowest loss. We also compared the proposed 2D-CNN model with a conventional one-dimensional CNN model. Comparison results show that the 1D-CNN classifier can achieve an averaged accuracy of 90.93%. Therefore, it is validated that the proposed CNN classifier using ECG spectrograms as input can achieve improved classification accuracy without additional manual pre-processing of the ECG signals.

Genetik Algoritmalar Kullanarak EKG Vuru Sınıflandırması için Öznitelik Seçimi

In this study, genetic algorithm method was used to select the most suitable set of features for classification of arrhythmia types of heart beats. Normal, right branch block, left branch block and pace rhythm samples of electrocardiography (ECG) signals which obtained from the MIT-BIH cardiac arrhythmia database were used in the classification. Mean, standard deviation, energy and entropy of discrete wavelet transform (DWT) coefficients were proposed as the features for the classification. By using the proposed DWT method, 16 features which have high classification accuracy were obtained among the 208 feature sets constructed from 13 different wavelet types by applying the genetic algorithm method. It was observed that the features that increase accuracy can be detected by the genetic algorithm and the feature set obtained from the coefficients of the different types of wavelets selected at different levels show higher performance than the coefficients obtained from the standard individual wavelet in the ECG arrhythmia classification.

Comparative analysis of MABC with KNN, SOM, and ACO algorithms for ECG heartbeat classification

In this paper, we proposed a classification method based on a nature-inspired algorithm, i.e., modified artificial bee colony (MABC). This method was applied to electrocardiogram (ECG) heartbeat classification. ECG data was obtained from MITBIH database. Eight different types of heartbeats (N, j, V, F, f, A, a, and R) were analyzed. For a better classification result, both time domain and frequency domain features were used. Feature selection was done by divergence analysis. MABC classification accuracy and heartbeat sensitivity values were compared with the results of other methods. Among other classifiers, k-nearest neighbor (KNN), Kohonen's self-organizing map (SOM), and ant colony optimization (ACO) were the best performing ones, and therefore their results are presented. The MABC classifier achieved 97.18 % accuracy on the analyzed dataset, as well as high sensitivity values for heartbeat types.