Heartbeat Classification Research Articles

ECG BEAT DETECTION AND CLASSIFICATION SYSTEM USING WAVELET TRANSFORM AND ONLINE SEQUENTIAL ELM

Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.

Feature Enrichment Based Convolutional Neural Network for Heartbeat Classification From Electrocardiogram

Correct heartbeat classification from electrocardiogram (ECG) signals is fundamental to the diagnosis of arrhythmia. The recent advancement in deep convolutional neural network (CNN) has renewed the interest in applying deep learning techniques to improve the accuracy of heartbeat classification. So far, the results are not very exciting. Most of the existing methods are based on ECG morphological information, which makes deep learning difficult to extract discriminative features for classification. Towards an opposite direction of feature extraction or selection, this paper proceeds along a recent proposed direction named feature enrichment (FE). To exploit the advantage of deep learning, we develop a FE-CNN classifier by enriching the ECG signals into time-frequency images by discrete short-time Fourier transform and then using the images as the input to CNN. Experiments on MIT-BIH arrhythmia database show FE-CNN obtains sensitivity (Sen) of 75.6%, positive predictive rate (Ppr) of 90.1%, and F1 score of 0.82 for the detection of supraventricular ectopic (S) beats. Sen, Ppr, and F1 score are 92.8%, 94.5%, and 0.94, respectively, for ventricular ectopic (V) beat detection. The result demonstrates our method outperforms state-of-the-art algorithms including other CNN based methods, without any hand-crafted features, especially F1 score for S beat detection from 0.75 to 0.82. This FE-CNN classifier is simple, effective, and easy to be applied to other types of vital signs.

Wavelets for Electrocardiogram: Overview and Taxonomy

Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of “wavelets for ECG” in the future.

Automated Heartbeat Classification Exploiting Convolutional Neural Network With Channel-Wise Attention

Long-term Electrocardiogram (ECG) analysis has become a common means of diagnosing cardiovascular diseases. In order to reduce the workload of cardiologists and accelerate diagnosis, an automated patient-specific heartbeat classification method based on a customized convolutional neural network (CNN) is proposed in this paper. The parallel convolutional layers with kernels of different receptive fields in the network are responsible for extracting multi-spatial deep features of the heartbeats, and the channel-wise attention module is adopted to selectively emphasize the informative features, which are beneficial to distinguish different classes of beats. To facilitate the extraction and emphasis of the important features, each heartbeat is segmented and stacked to form the multi-channel network input according to the basic temporal characteristics of the main components (P wave, QRS complex, and T wave) in the ECG. Besides, to further improve the network generalization and achieve better performance on various ECG of new patients, a method of intra-record sample clustering is proposed to select the representative heartbeats to construct the training set. The proposed method classifies heartbeats into five classes (normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unclassifiable beat (Q)). Validated on the MIT-BIH arrhythmia database, our approach demonstrates performance superior to several state-of-the-art methods. In addition to an average accuracy and specificity of over 99%, this method achieves a sensitivity of 95.4% and a positive predictivity of 97.1% for VEB class, and a sensitivity of 81.1% and a positive predictivity of 90.0% for SVEB class. With high classification performance and pathological heartbeat detection accuracy, the proposed method is promising for clinical device applications.

Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Coronary artery disease (CAD) has been one of main causes of heart diseases globally. The electrocardiogram (ECG) is a widely used diagnostic tool to monitor patients’ heart activities, and medical personnel need to judge whether there are abnormal heartbeats according to captured results. Therefore, it is significant to identify ECG signals accurately and fast. In this paper, a fast and accurate electrocardiogram (ECG) classification system based on deep learning is proposed. In our model, stacked denoising autoencoders (SDAE), as encoder, automatically learns semantic encoding of heartbeats without any complex feature extraction in unsupervised way. Then bidirectional LSTM (Bi-LSTM) classifier achieves classification of heartbeats with semantic encoding. SDAE implements noise-reduction while Bi-LSTM takes full advantage of temporal information in data. At the same time, this method relieves impacts from unbalanced data by employing cost-sensitive loss function. We validate our model on MIT-BIH Arrhythmias Database, SVDB and NSTDB respectively. Compared with state-of-art methods, the final result verify that this newly proposed method not only has high accuracy but also boosts classifying efficiency.

Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network

Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with heartbeat activity's global sequence features can effectively improve the accuracy of heartbeat classification. Firstly, the noise is removed by the continuous wavelet transform method. Secondly, the peak of the R wave is detected by the tagged database, and then the P-QRS-T wave morphology and the RR interval are extracted. This feature set is heartbeat activity's global sequence features, which combines single heartbeat morphology and 21 consecutive RR intervals. Finally, the Bi-LSTM algorithm and the BiLSTM-Attention algorithm are used to identify heartbeat category respectively, and the MIT-BIH arrhythmia database is used to verify the algorithm. The results show that the BiLSTM-Attention model combined with heartbeat activity's global sequence features has higher interpretability than other methods discussed in this paper.

A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments

In this paper, we propose a new automated quality-aware electrocardiogram (ECG) beat classification method for effective diagnosis of ECG arrhythmias under unsupervised healthcare environments. The proposed method consists of three major stages: 1) the ECG signal quality assessment (“ acceptable ” or “ unacceptable ”) based on our previous modified complete ensemble empirical mode decomposition and temporal features; 2) the ECG signal reconstruction and R-peak detection; and 3) the ECG beat classification including the ECG beat extraction, beat alignment, and normalized cross-correlation-based beat classification. The accuracy and robustness of the proposed method are evaluated using different normal and abnormal ECG signals taken from the standard MIT-BIH arrhythmia database. Evaluation results show that the proposed quality-aware ECG beat classification method can significantly achieve false alarm reduction ranging from 24% to 93% under noisy ECG recordings. The R-peak detector achieves the average Se = 99.67% and positive predictivity (Pp) = 93.10% and the average sensitivity (Se) = 99.65% and Pp = 98.88% without and with denoising approaches, respectively. Results further showed that the proposed ECG beat extraction approach can improve the classification accuracy by preserving the QRS complex portion and suppressing the background noises under acceptable level of noises. The quality-aware ECG beat classification methods achieve higher kappa values for the classification accuracies which can be consistent as compared with the heartbeat classification methods without the ECG quality assessment process.

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.

Automated Heartbeat Classification Using 3-D Inputs Based on Convolutional Neural Network With Multi-Fields of View

A high-performance method of automated heartbeat classification based on Convolutional Neural Network (CNN) is proposed in this paper. To make full use of the electrocardiogram information acquired from different parts of the human body, we present a novel 3-D data structure as the input of the CNN. The 3-D structure consists of multiple feature maps, each of which indicates the information collected from one lead and contains morphological characteristic, RR-interval, and beat-to-beat correlation feature. Besides, atrous spatial pyramid pooling (ASPP) module which uses filters with different resolutions is adopted to extract deep features in multi-fields of view. Validated on the MIT-BIH arrhythmia database, the proposed method yields an overall accuracy of 91.44% in the inter-patient practice. In particular, this method achieves 89.05% and 95.15% in the sensitivities of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) classes, respectively. With the high performance in detecting these two pathological classes, this method has potential clinical application.

A robust deep convolutional neural network with batch-weighted loss for heartbeat classification

The early detection of abnormal heart rhythm has become crucial due to the spike in the rate of deaths caused by cardiovascular diseases. While many existing works tried to classify heartbeats accurately, they suffered from the imbalance between heartbeat classes in the available ECG datasets since abnormal heartbeats appear much less frequently than normal ones. In addition, most of existing methods heavily rely on data preprocessing such as noise removal and feature extraction, which is computationally expensive, thus limits their use on low-cost portable ECG devices.We present a novel deep convolutional neural network based on state-of-the-art deep learning techniques for accurate heartbeat classification. We suggest a batch-weighted loss function to better quantify the loss in order to overcome the imbalance between classes. The loss weights dynamically change as the distribution of classes in each batch changes. Also, we propose to use multiple heartbeats for more effective heartbeat classification.Even though we use ECG signal from one lead only without any data preprocessing, our method consistently outperforms existing methods of 5-class heartbeat classification. Our accuracy, positive productivity, sensitivity and specificity under intra-patient paradigm are 99.48%, 98.83%, 96.97% and 99.87%, and those under inter-patient paradigm are 88.34%, 48.25%, 90.90% and 88.51% respectively.

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.

Localization and classification of heartbeats using robust adaptive algorithm

Abstract Automated analysis of heart sound recordings (phonocardiograms or PCG) for classification of heart beats in noisy environments is a challenging problem. The major challenges are introduced due to inter- and intra-beat variation, spectral overlap of heart and lung sounds and noise picked from environment. This paper presents a robust algorithm for heart beat localization and classification using variable length window and peak amplitude threshold variation. The technique presented in this paper first attempts to find peaks which correspond to the fundamental heart sound cycle (FHSC) i.e. S1, S2, S1, … or fundamental heart sound period (FHSP) i.e. S1, S1, …. If FHSC/FHSP is found, an attempt is made to extend it to the whole signal. Once extended, the signal is directly labeled using systole and diastole durations. During classification, history of labels is used to counter the heart rate variations. The proposed technique is evaluated on publicly available Pascal Heart Sound Challenge dataset and reports smaller average error for localization and has high sensitivity and accuracy for heart beat classification.

Automated real-time method for ventricular heartbeat classification

Background and objective: In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity.Methods: The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training.Results: The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads.Conclusions: The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes.

Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection

The electrocardiogram (ECG) is relevant for several medical purposes. In this work, a novel analysis-by-synthesis method to process ECG signals is presented. It is based on the matching pursuit algorithm, which is employed here to decompose the ECG in the time domain. The main features of the ECG are extracted through a dictionary of triangular functions, due to their good correlation with the typical electrocardiographic waveforms, especially the R wave. The individual elements of this signal representation can be further employed for different processing tasks, such as ECG compression and QRS detection. Compression is required to store and transmit signals in situations related to massive acquisitions, frequent monitoring, high-resolution data, real-time needs or narrow bandwidths. QRS detection is not only essential to study the heart rate variability, but also the basis of automatic systems for ECG applications such as heartbeat classification or anomaly identification. In this study, it is shown how to employ the proposed processing approach to perform ECG compression and beat detection jointly. The resulting algorithm is tested over the whole MIT-BIH Arrhythmia Database, with a wide variety of ECG records, yielding both high compression and efficient QRS detection.

Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks.

This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points are extracted from raw ECG signals, and consecutive vectors are extracted from a sliding time-window covering these sample points. Each of these vectors comprises the consecutive sample points of a complete heartbeat cycle, which includes not only the QRS complex but also the P and T waves. Unlike existing heartbeat classification methods in which medical doctors extract handcrafted features from raw ECG signals, the proposed end-to-end method leverages a deep neural network for both feature extraction and classification based on aligned heartbeats. This strategy not only obviates the need to handcraft the features but also produces optimized ECG representation for heartbeat classification. Evaluations on the MIT-BIH arrhythmia database show that at the same specificity, the proposed patient-independent classifier can detect supraventricular- and ventricular-ectopic beats at a sensitivity that is at least 10% higher than current state-of-the-art methods. More importantly, there is a wide range of operating points in which both the sensitivity and specificity of the proposed classifier are higher than those achieved by state-of-the-art classifiers. The proposed classifier can also perform comparable to patient-specific classifiers, but at the same time enjoys the advantage of patient independence.

Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine

Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewed data. To address these problems, a novel heartbeat recognition method is presented. The aim of this study is to apply a principal component analysis network (PCANet) for feature extraction based on a noisy ECG signal. To improve the classification speed, a linear support vector machine (SVM) was applied. In our experiments, we identified five types of imbalanced original and noise-free ECGs in the MIT-BIH arrhythmia database to verify the effectiveness of our algorithm and achieved 97.77% and 97.08% accuracy, respectively. The results show that our method has high recognition accuracy in the classification of skewed and noisy heartbeats, indicating that our method is a practical ECG recognition method with suitable noise robustness and skewed data applicability.

Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACTA new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.

Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

Heartbeat classification with low computational cost using Hjorth parameters

A method for electrocardiogram (ECG) feature extraction is presented for automatic classification of heartbeats, using values of RR intervals, amplitude and Hjorth parameters. Hjorth parameters have been used in a variety of research areas, but their application to ECG signal processing is still little explored. This paper also introduces a new approach to heartbeat segmentation, which avoids mixing information from adjacent beats and improves classification performance. The proposed model is validated in the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia database and presents an overall accuracy of 90.4%, better than other state-of-the-art methods. There is an improvement over other models in positive predictivity for class S (66.6%) of supraventricular ectopic beats, and sensitivity for class N (93.0%). Results obtained indicate that the techniques used in this study can be successfully applied to the problem of automatic heartbeat classification. In addition, this new approach has low computational cost, which allows its later implementation in hardware devices with limited resources.

Hybrid hierarchical method for electrocardiogram heartbeat classification

This paper proposes an automatic reliable two-stage hybrid hierarchical method for ECG heartbeat classification. The heartbeats are segmented dynamically to avoid the consequences of the heart rate variability. Discrete Wavelet Transform (DWT) is utilized to extract morphological features. The extracted features are then reduced by using Principle Component Analysis (PCA). Subsequently, the resulted features along with four RR features are fed into Support Vector Machine (SVM) to classify five categories. Thereafter, the heartbeats are further classified to one of the classes belonging to the assigned category. Two different strategies for classification have been investigated: One versus All and One versus One. The proposed method has been applied on data from lead 1 and lead 2. A new fusion step is introduced, where stacked generalisation algorithm is applied and different types of classifiers have been examined. Experiments have been carried out using a MIT_BIH database. The best overall and average accuracies obtained by the first stage are 98.40% and 97.50% respectively. For the second stage, 94.94% and 93.19% are the best overall and average accuracies obtained respectively. The best results are achieved using SVM with one versus one classification strategy for both stages and decision trees classifier for the fusion step.