Heartbeat Classification Research Articles

A Granular Resampling Method and Adaptive Speculative Mechanism-Based Energy-Efficient Architecture for Multiclass Heartbeat Classification

This brief presents an energy-efficient design with cascaded structure aiming at multiclass heartbeat classification. support vector machine-based granular resampling method is put forward to obtain a hybrid classifier which includes a low-complexity model (LCM) to identify most easy-to-learn heartbeats and a high-accuracy classifier to discriminate the remained. The hybrid classifier combined with one-versus-all strategy is employed to achieve a multiclass classification model. An adaptive speculative mechanism based on the occurrence regularity of electrocardiogram abnormities is proposed to lower the complexity and computation burden of the multiclass classification model. The corresponding energy-efficient hardware architecture is designed and its architecture optimizations include memory segmentation to reduce energy consumption and time domain reuse to save resources. Implemented in 40-nm CMOS process, the design occupies 0.135 mm2 area. It consumes 2.60–48.99 nJ/classification under 1-V voltage supply and 1 MHz operating frequency. Results show that the design provides an average prediction speedup by 60.66% and a significant energy dissipation reduction by 55.26% per beat compared with a high-accuracy model without LCMs.

Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks.

This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of the heartbeat and alleviate the influence of inter-patient variation through baseline correction. The symbolic representation of the heartbeat is used by a multi-perspective convolutional neural network (MPCNN) to learn features automatically and classify the heartbeat. We evaluate our method for the detection of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) on MIT-BIH arrhythmia dataset. Compared with the state-of-the-art methods based on manual features or deep learning models, our method shows superior performance: the overall accuracy of 96.4%, F1 scores for SVEB and VEB of 76.6% and 89.7%, respectively. The ablation study on our method validates the effectiveness of the proposed symbolization approach and joint representation architecture, which can help the deep learning model to learn more general features and improve the ability of generalization for unseen patients. Because our method achieves a competitive inter-patient heartbeat classification performance without complex handcrafted features or the intervention of the human expert, it can also be adjusted to handle various other tasks relative to ECG classification.

Automated heartbeat classification based on deep neural network with multiple input layers

The arrhythmia is an important group of cardiovascular disease. Electrocardiogram (ECG) is commonly used for detecting arrhythmias. Computer-aided diagnosis system can diagnose ECG automatically without the limitations of visual inspection. In order to improve the performance of ECG heartbeat classification, this paper proposes a novel automatic classification system. Based on convolutional neural network (CNN) and long short-term memory (LSTM) network, a deep structure with multiple input layers is proposed. Four input layers are constructed based on different regions of a heartbeat and RR interval features. The first three inputs are convolved using different strides. The three outputs of CNN are then concatenated and go through an LSTM network. Two fully-connected layers follow and the output is concatenated with the fourth input. Eventually, the last fully-connected layer outputs the predicted label. The proposed system was evaluated by two division schemes of the MIT-BIH arrhythmia database. Class-oriented scheme achieved an overall accuracy of 99.26% and subject-oriented scheme obtained an accuracy of 94.20%. The comparison with previous works showed the excellent performance of the novel network. The combination of automatic features and handcraft features was demonstrated to be helpful in heartbeat classification. Hence, the system can be used for clinical application.

Analysis and classification of heart diseases using heartbeat features and machine learning algorithms

This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the heart. The proposed approach is implemented using ML-libs and Scala language on Apache Spark framework; MLlib is Apache Spark’s scalable machine learning library. The key challenge in ECG classification is to handle the irregularities in the ECG signals which is very important to detect the patient status. Therefore, we have proposed an efficient approach to classify ECG signals with high accuracy Each heartbeat is a combination of action impulse waveforms produced by different specialized cardiac heart tissues. Heartbeats classification faces some difficulties because these waveforms differ from person to another, they are described by some features. These features are the inputs of machine learning algorithm. In general, using Spark–Scala tools simplifies the usage of many algorithms such as machine-learning (ML) algorithms. On other hand, Spark–Scala is preferred to be used more than other tools when size of processing data is too large. In our case, we have used a dataset with 205,146 records to evaluate the performance of our approach. Machine learning libraries in Spark–Scala provide easy ways to implement many classification algorithms (Decision Tree, Random Forests, Gradient-Boosted Trees (GDB), etc.). The proposed method is evaluated and validated on baseline MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia database. The results show that our approach achieved an overall accuracy of 96.75% using GDB Tree algorithm and 97.98% using random Forest for binary classification. For multi class classification, it achieved to 98.03% accuracy using Random Forest, Gradient Boosting tree supports only binary classification.

A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection appr

Decomposition of higher-order spectra for blind multiple-input deconvolution, pattern identification and separation

Like the ordinary power spectrum, higher-order spectra (HOS) describe signal properties that are invariant under translations in time. Unlike the power spectrum, HOS retain phase information from which details of the signal waveform can be recovered. Here we consider the problem of identifying multiple unknown transient waveforms which recur within an ensemble of records at mutually random delays. We develop a new technique for recovering filters from HOS whose performance in waveform detection approaches that of an optimal matched filter, requiring no prior information about the waveforms. Unlike previous techniques of signal identification through HOS, the method applies equally well to signals with deterministic and non-deterministic HOS. In the non-deterministic case, it yields an additive decomposition, introducing a new approach to the separation of component processes within non-Gaussian signals having non-deterministic higher moments. We show a close relationship to minimum-entropy blind deconvolution (MED), which the present technique improves upon by avoiding the need for numerical optimization, while requiring only numerically stable operations of time shift, element-wise multiplication and averaging, making it particularly suited for real-time applications. The application of HOS decomposition to real-world signals is demonstrated with blind denoising, detection and classification of normal and abnormal heartbeats in electrocardiograms.

Are we training our heartbeat classification algorithms properly?

Despite the multiple studies dealing with heartbeat classification, the accurate detection of Supraventricular heartbeats (SVEB) is still very challenging. Therefore, this study aims to question the current protocol followed to report heartbeat classification results, which impedes the improvement of the SVEB class without falling on over-fitting. In this study, a novel approach based on Variational Mode Decomposition (VMD) as source of features is proposed, and the impact of the use of the MIT-BIH Arrhythmia database is analyzed.The method proposed is based on single-lead electrocardiogram, and it characterizes heartbeats by a set of 45 features: 5 related to the time intervals between consecutive heartbeats, and the rest related to VMD. Each heartbeat is decomposed in their variational modes, which are, on their turn, characterized by their frequency content, morphology and higher order statistics. The 10 most relevant features are selected using a backwards wrapper feature selector, and they are fed into an LS-SVM classifier, which is trained to separate Normal (N), Supraventricular (SVEB), Ventricular (VEB) and Fusion (F) heartbeats. An inter-patient approach, using patient independent training, is considered as suggested in the literature.The method achieves sensitivities above 80% for the three most important classes of the database (N, SVEB and VEB), and high specificities for the N and VEB classes. Given the challenges related to the SVEB and F class present in the literature, the composition of the MIT-BIH database is analyzed and alternatives are suggested in order to train heartbeat classification algorithms in a novel and more realistic way.

A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines.

Real-time wearable electrocardiogram monitoring sensor is one of the best candidates in assisting cardiovascular disease diagnosis. In this paper, we present a novel real-time machine learning system for Arrhythmia classification. The system is based on the parallel Delta modulation and QRS/PT wave detection algorithms. We propose a patient dependent rotated linear-kernel support vector machine classifier that combines the global and local classifiers, with three types of feature vectors extracted directly from the Delta modulated bit-streams. The performance of the proposed system is evaluated using the MIT-BIH Arrhythmia database. According to the AAMI standard, two binary classifications are performed and evaluated, which are supraventricular ectopic beat (SVEB) versus the rest four classes, and ventricular ectopic beat (VEB) versus the rest. For SVEB classification, the preferred SkP-32 method's F1 score, sensitivity, specificity, and positive predictivity value are 0.83, 79.3%, 99.6%, and 88.2%, respectively, and for VEB classification, the numbers are 0.92%, 92.8%, 99.4%, and 91.6%, respectively. The results show that the performance of our proposed approach is comparable to that of published research. The proposed low-complexity algorithm has the potential to be implemented as an on-sensor machine learning solution.

Arrhythmia Detection from ECG based Heartbeat Classification using Deep Learning Networks

Arrhythmia Detection from ECG based Heartbeat Classification using Deep Learning Networks

Automated ECG heartbeat classification by combining a multilayer perceptron neural network with enhanced particle swarm optimization algorithm

The electrocardiograms analysis helps cardiologists to diagnosis different cardiac disorders. In this paper, we have proposed a new training algorithm of a multilayer perceptron (MLP) neural network to improve the classification task of arrhythmias. For this purpose, an enhanced particle swarm optimization algorithm (EPSO) was implemented by proposing a new updating equation of the inertia weight factor through iterations. In this work, five predominant types of ECG beats from MIT-BIH database are taken as desired output classes of the MLP neural network. The considered heartbeats are normal (N), premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), and left bundle branch block (LBBB). The accurate classification and recognition of different classes have been focused on the extraction of the more relevant and appropriate feature parameters. For this purpose, we have implemented the classifier using several kinds of features (morphological, wavelet, or statistical features) and also with different combinations of them in order to select the more pertinent features to the studied classes of ECG beats. The evaluation of the proposed EPSO-MLPNN classification system on ECG data from MIT-BIH database achieves the good classification results in terms of three statistical parameters. We have computed a specificity of 99.81%, a sensitivity of 97.67%, and an accuracy of 98.56%. Furthermore, these results demonstrate a clear improvement in the classification performances compared with those of other methods of classification carried out on MIT-BIH database. The proposed classification system can be used as a powerful tool in the field of arrhythmia diagnostic.

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification.

Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. To address this issue, this paper proposes adapting a patient-independent deep neural network (DNN) using the information in the patient-dependent identity vectors (i-vectors). The adapted networks, namely i-vector adapted patient-specific DNNs (iAP-DNNs), are tuned toward the ECG characteristics of individual patients. For each patient, his/her ECG waveforms are compressed into an i-vector using a factor analysis model. Then, this i-vector is injected into the middle hidden layer of the patient-independent DNN. Stochastic gradient descent is then applied to fine-tune the whole network to form a patient-specific classifier. As a result, the adaptation makes use of not only the raw ECG waveforms from the specific patient but also the compact representation of his/her ECG characteristics through the i-vector. Analysis on the hidden-layer activations shows that by leveraging the information in the i-vectors, the iAP-DNNs are more capable of discriminating normal heartbeats against arrhythmic heartbeats than the networks that use the patient-specific ECG only for the adaptation. Experimental results based on the MIT-BIH database suggest that the iAP-DNNs perform better than existing patient-specific classifiers in terms of various performance measures. In particular, the sensitivity and specificity of the existing methods are all under the receiver operating characteristic curves of the iAP-DNNs.

Next-generation heartbeat classification with a column-store DBMS and UDFs

We live in a digital world where data is being generated at an always increasing rate. This creates the need to develop new technology not only for storing these vast amounts of data, but also for manipulating and analyzing it. It is through this data analysis that we can make decisions and generate knowledge. The medical field is no exception and healthcare and biomedical data must be stored and analyzed to gain insights that help in disease prevention and diagnostics. An example of this kind of data are electrocardiograms (ECG), whose careful analysis has proven to be of significant help to diagnose cardiovascular abnormalities. ECG recording devices can produce a very large amount of data in a short period of time. Usually abstracted as unstructured data, ECG digital signals have traditionally been stored and analyzed using file-based solutions for storage, and ad-hoc programs for data processing. We favor the idea that ECG signals can be abstracted as sets of tuples and stored in database relations. In this paper we present a proposal to store, manage, and analyze ECG data in a column-store database management system (DBMS). We provide extensive empirical evidence showing that incorporating complex analytical tasks such as ECG transformation and classification into a DBMS is not only feasible but also efficient and scalable. For this, we rely on the Structured Query Language provided by relational DBMSs, and the implementation of user defined functions.

Inter- and intra-patient ECG heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach.

Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that our method achieves the best performance in the literature (a positive predictive value of 96.46% and sensitivity of 100% for the category S, and a positive predictive value of 98.68% and sensitivity of 97.40% for the category F for the intra-patient scheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the category S, and a positive predictive value of 99.50% and sensitivity of 99.94% for the category V for the inter-patient scheme.).

Classification of Electrocardiogram Signals using Principal Component Analysis and Levenberg Marquardt Backpropagation for Detection Ventricular Tachyarrhythmia

Abstract
 Ventricular Tachyarrhythmia (VT) are the primary arrhythmias which are cause of sudden death. For someone who already has symptoms of VT should immediately perform an examination of one of them by using an electrocardiogram (ECG). An electrocardiogram is a recording of the heart's electrical results in a waveform. However, limited ability in analysis and diagnosis of ECG reading is still difficult to do. Therefore, the classification of ECG signals is needed to detect a person, especially those with VT or not. In this research focuses on the classification of VT heartbeats from ECG signals by using median filter method in preprocessing, Principal Component Analysis (PCA) as feature extraction and modified Backpropagation (MBP) as classification. This research used machine learning method that is a neural network with backpropagation modification that is Levenberg Marquardt to speed up network training process. The best VT detection performance results were based on the average accuracy of the overall scheme of 91.67% with the best parameters that principal component=10 and 20, hidden neuron=4, and µ value=0.001 as well training time 1 seconds with a comparison of train data and test data that is 80:20 percent.
 Keywords: Electrocardiogram, Levenberg Marquardt Backpropagation, Median filter, Principal Component Analysis, and Ventricular Tachyarrhythmia

A novel multi-module neural network system for imbalanced heartbeats classification

In this paper, a novel multi-module neural network system named MMNNS is proposed to solve the imbalance problem in electrocardiogram (ECG) heartbeats classification. Four submodules are designed to construct the system: preprocessing, imbalance problem processing, feature extraction and classification. Imbalance problem processing module mainly introduces three methods: BLSM, CTFM and 2PT, which are proposed from three aspects of resampling, data feature and algorithm respectively. BLSM is used to synthesize virtual samples linearly around the minority samples. CTFM consists of DAE-based feature extraction part and QRS-based feature selection part, in which selected features and complete features are applied to determine the heartbeat class simultaneously. The processed data are fed into a convolutional neural network (CNN) by applying 2PT to train and fine-tune. MMNNS is trained on MIT-BIH Arrhythmia Database following AAMI standard, using intra-patient and inter-patient scheme, especially the latter which is strongly recommended. The comparisons with several state-of-the-art methods using standard criteria on three datasets demonstrate the superiority of MMNNS for improving detection of heartbeats and addressing imbalance in ECG heartbeats classification.

Learning vector quantization and relevances in complex coefficient space

In this contribution, we consider the classification of time series and similar functional data which can be represented in complex Fourier and wavelet coefficient space. We apply versions of learning vector quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger calculus. It allows for the formulation of gradient-based update rules in the framework of cost-function-based generalized matrix relevance LVQ (GMLVQ). Alternatively, we consider the concatenation of real and imaginary parts of Fourier coefficients in a real-valued feature vector and the classification of time-domain representations by means of conventional GMLVQ. In addition, we consider the application of the method in combination with wavelet-space features to heartbeat classification.

Inter-patient heartbeat classification based on region feature extraction and ensemble classifier

The electrocardiogram (ECG) is an important tool for detecting arrhythmia. To solve the limitations of visual inspection, computer-aided diagnosis appears and grows rapidly. Most of the reported researches for heartbeat classification were based on intra-patient dataset. Moreover, existing inter-patient researches were usually conducted for superclasses of arrhythmia. To classify specific types of arrhythmia, this study proposed an inter-patient heartbeat classification method based on region feature extraction and ensemble classifier. The proposed method is composed of four stages. In preprocessing stage, the ECG signal is filtered and proportionally segmented. Afterwards, heartbeats are divided into three regions and region features are extracted. Subsequently, the dimension of features is reduced and all the features are fused and normalized. Eventually, an ensemble classifier is employed for the classification of Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). The method was applied to a new dataset divided from MIT-BIH arrhythmia database. The obtained sensitivities for Normal, LBBB, RBBB, APV and VPC were 95.0%, 27.9%, 79.6%, 81.8% and 88.1%. A comparative experiment demonstrated that the proposed region feature extraction method improves the accuracy of arrhythmia classification. The new division of MIT-BIH arrhythmia database is also advised to other researchers.

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification

Background and objectiveElectrocardiogram (ECG) is a useful tool for detecting heart disease. Automated ECG diagnosis allows for heart monitoring on small devices, especially on wearable devices. In order to recognize arrhythmias automatically, accurate classification method for electrocardiogram (ECG) heartbeats was studied in this paper. MethodsBased on weighted extreme gradient boosting (XGBoost), a hierarchical classification method is proposed. A large number of features from 6 categories are extracted from the preprocessed heartbeats. Then recursive feature elimination is used for selecting features. Afterwards, a hierarchical classifier is constructed in classification stage. The hierarchical classifier is composed of threshold and XGBoost classifiers. And the XGBoost classifiers are improved with weights. ResultsThe method was applied to an inter-patient experiment conforming AAMI standard. The obtained sensitivities for normal (N), supraventricular (S), ventricular (V), fusion (F), and Unknown beats (Q) were 92.1%, 91.7%, 95.1%, and 61.6%. Positive predictive values of 99.5%, 46.2%, 88.1%, and 15.2% were also provided for the four classes. ConclusionsXGBoost was improved and firstly introduced in single heartbeat classification. A comparison showed the effectiveness of the novel method. The method was more suitable for clinical application as both high positive predictive value for N class and high sensitivities for abnormal classes were provided.

DEMONSTRATING ELECTROCARDIOGRAM (ECG) SIGNAL PROCESSING TO SHOW NONLINEAR ABNORMALITIES OF THE HEART

Heart beat classification is considered as the main tool for recognizing and diagnosing different heart diseases. The shape and size of these waves, the time between each wave and the rate and regularity of the heartbeat provide valuable information to doctors. An electrocardiogram, also called an ECG or EKG, is a quick, painless test that measures the heart’s electrical activity and records any disturbances in heart rhythm. The paper aimed at demonstrating electrocardiogram (ECG) signal processing to show the nonlinear abnormalities of the heart using Microsoft Excel. The study will definitely enhance fast and cost effective diagnosis of Ischemic Episodes.

Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

The abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems.