Automatic Arrhythmia Detection Research Articles

Unsupervised semantic-aware adaptive feature fusion network for arrhythmia detection

An electrocardiogram (ECG) consists of complex P-QRS-T waves. Detecting long-term ECG recordings is time-consuming and error-prone for cardiologists. Deep neural networks (DNNs) can learn deep representations and empower automatic arrhythmia detection. However, when applying DNNs in practice, they usually suffer from domain shift that exits between the training data and testing data. Such shift can be caused by the high variability contained in ECG signals between patients and internal-variability of heartbeats for same patients, leading to degrading performance and impeding generalization of DNNs. To tackle this problem, we propose an unsupervised semantic-aware adaptive feature fusion network (USAFFN) to reduce such shift by alleviating the semantic distribution discrepancy between the feature spaces of two domains. Furthermore, an ECG contains rich information from different angles (beat, rhythm, and frequency levels), which is essential for arrhythmia detection. Therefore, a multi-perspective adaptive feature fusion (MPAFF) module is introduced to extract informative ECG representations. The experimental results show that the detection performance of our approach is highly competitive with the upper bound of alternative methods on the ARDB, and the generalization is confirmed on the INCART and LTDB.

Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

Automatic arrhythmia detection in electrocardiogram (ECG) using supervised learning has gained significant considerations in recent years. This paper projects the performance analysis of classifiers such as support vector machine (SVM), extreme learning machine (ELM), and k-nearest neighbor (KNN) with efficient time utilization showing multi-classification for specific medical application. The wavelet double decomposition is used to show the shift-invariant use of dual-tree complex wavelet transform for noise filtering and beat segmentation is done to extract 130 informative samples. Further, the linear discriminant analysis is applied to dimensionally reduce and elite the 12 most relevant features for classifying normal and four abnormal beats collected from MIT/BIH ECG database. The proposed executed system distinguishes SVM, ELM, and KNN with percentage accuracy of 99.8, 97, and 99.8 having classifier testing time as 0.0081s, 0.0031s, and 0.0234s, respectively. The simulated experimental outcomes in comparison with existing work yields adequate accuracy, and computational time.

Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records

Background and objectiveArrhythmia constitute a common clinical problem in cardiology. The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG interpretation by experts is expensive and time-consuming. In this work, we developed and validated an arrhythmia classification model based on handcrafted features, which was more computationally efficient than traditional deep learning models. Material and methodThe classification model comprised (i) a specific feature extraction function based on the homeomorphically irreducible tree (HIT) graph pattern, (ii) multilevel feature generation based on maximum absolute pooling, (iii) Chi2 feature selector, and (iv) standard support vector machine classifier. We trained and validated the model on a large dataset comprising 12-leads ECGs acquired from more than 10,000 subjects. Performance metrics were reported for seven- (Case 1) and four-class (Case 2) arrhythmia diagnosis. ResultsHigh classification accuracy rates of 92.95% and 97.18% were attained for Case 1 and Case 2, respectively, that were comparable with those of deep learning on the same ECG dataset. ConclusionThe model achieved excellent classification results at low computational cost, which underscores the potential for real world application of the proposed HIT-based ECG classification model.

Automated arrhythmia detection from electrocardiogram signal using stacked restricted Boltzmann machine model

Significant advances in deep learning techniques have made it possible to offer technologically advanced methods to detect cardiac abnormalities. In this study, we have proposed a new deep learning based Restricted Boltzmann machine (RBM) model for the classification of arrhythmias from Electrocardiogram (ECG) signal. The work is divided into three phases where, in the first phase, signal processing is performed, including the normalization of the heartbeats as well as the segmentation of the heartbeats. In the second phase, the stacked RBM model is implemented which extracts the essential features from the ECG signal. Finally, a SoftMax activation function is used that classifies the ECG signal into four types of heartbeat classes according to ANSI/AAMA standards. This stacked RBM model is offered as three types of experiments, patient independent data classification for multi-class, patient independent data for binary classification, and patient specific classification. The best result was obtained using patient independent binary classification with an overall accuracy of 99.61%. For Patient Independent Multi Class classification, accuracy obtained was 98.61% and for patient specific data, the accuracy was 95.13%. The experimental results shows that the developed RBM model has better performance in terms of accuracy, sensitivity and specificity as compared to work mentioned in the other research papers.Article highlightsThe proposed RBM model is skilled to automatically classify ECG heartbeat according to the ANSI- AAMI standards with accuracy, Recall, specificity.The performance of the RBM model to correctly classify heartbeat classes was found to be improved.The model is fully automatic, hence there is no requirement of additional system like feature extraction, feature selection, and classification.

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG.

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average n}{}F_{1}n score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

Deep Learning Based Arrhythmia Recognition: A review

Sudden cardiac death is one of the major causes of mortality in the entire world. Cardiovascular diseases (CVDs) are much common nowadays irrespective of age, sex, and physic of the individuals. CVDs can trigger arrhythmias, the abnormal electrical activity of the heart. Arrhythmias are of different types and all of them are life-threatening and even may lead to sudden cardiac death, if not treated immediately. Abnormal electrical activity in the heart will reflect as altered waveforms with the morphological and dynamical change in electrocardiogram (ECG). Automatic detection and classification of arrhythmias using conventional algorithms and advanced machine learning techniques can increase the accuracy and speed of diagnosis, and thereby increase the overall efficiency of the health care system. In this review paper, the conventional arrhythmia detection techniques and three different machine learning techniques are compared. The comparison shows that the convolutional neural networks can classify all types of arrhythmias when trained and tested with the proper number of data sets as well as the choice of different methodologies depends upon the application.

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.

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.

P1362Diagnostic accuracy of R-wave detection by insertable cardiac monitors

Abstract OnBehalf 1.Department of Cardiology, Ghent University Hospital, Ghent, Belgium Introduction Insertable cardiac monitors (ICM) allow automatic arrhythmia detection but the diagnostic performance can be hampered by poor R-wave sensing leading to false positive recordings. Purpose This study assesses the prevalence and predictors of R-wave under- and oversensing among different ICM types. Methods Patients implanted with an ICM at the University Hospital of Ghent between January 2017 and August 2018 were included. All ICM tracings recorded at ICM interrogation or transmitted by remote monitoring were reviewed for inadequate R-wave sensing leading to false arrhythmia alerts. Inadequate R wave sensing was defined as undersensing due to sudden reduction in R wave amplitude or oversensing due to the occurrence of artefacts. Patient and implant characteristics were retrieved from the medical record and implant reports. Results The study screened 135 patients (age 59 ± 19 years, 44% female) implanted with different ICM types: Reveal XT and LINQ n = 92 (68%), Confirm and Confirm Rx n = 35 (26%), BioMonitor n = 8 (6%). ICM analysis was conducted in 112 patients (83%). Overall, false arrhythmia alerts due to inadequate R wave sensing occurred in 22 (20%) of the patients. Among these 22 patients, false diagnosis of bradycardia or pauses were documented in 64%, false high ventricular rates in 14% and false atrial fibrillation alerts in 22% of the patients. Of interest, in an additional 7 (6%) patients significant changes in R wave sensing occurred, although not causing false arrhythmic alerts. Occurrence of R-wave changes occurred in 19% of Reveal, 17% of Confirm and 50% of BioMonitor ICMs (p = not significant). Occurrence of R-wave changes among the different types of ICMs were not related to age, sex, BMI, time of the day, heart frequency or implant R-wave sensing. Conclusion Poor R wave sensing, leading to false arrhythmia detection, occurred in a significant number of ICM, and seems to occur independent of ICM type, patient or implant characteristics. Abstract Figure.

Short Term ECG Classification with Residual-Concatenate Network and Metric Learning

ECG classification is important to the diagnosis of cardiovascular disease. This paper develops a robust and accurate algorithm for automatic detection of heart arrhythmias from ECG signals recorded with one lead. A novel model based on the convolutional neural network is proposed to extract low-level and high-level features of short term ECG. In addition, Information-Theoretic Metric Learning is utilized as a final classification model to boost the discrimination abilities of the network trained features. The experimental results over the MIT-BIH arrhythmia database show that the model achieves a comparable performance with most of the state-of-the-art methods and Information-Theoretic Metric Learning further improves the performance. Besides the good accuracy achieved, the proposed method balances different criteria.

ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method for extracting features due to its local connectivity and parameter sharing. In addition, recurrent neural network (RNN) is another commonly used method, which is applied to process time-series signal. The stacking of both CNN and RNN has been proved to be more effective in multi-class arrhythmia detection. However, these networks ignored the fact that different channels and temporal segments of a feature map extracted from the 12-lead ECG signal contribute differently to cardiac arrhythmia detection, and thus, the classification performance could be greatly improved. To address this issue, spatio-temporal attention-based convolutional recurrent neural network (STA-CRNN) is proposed to focus on representative features along both spatial and temporal axes. STA-CRNN consists of CNN subnetwork, spatio-temporal attention modules and RNN subnetwork. The experiment result shows that, STA-CRNN reaches an average F1 score of 0.835 in classifying 8 types of arrhythmias and normal rhythm. Compared with the state-of-the-art methods based on the same public dataset, STA-CRNN achieves an obvious improvement on identifying most of arrhythmias. Also, it is demonstrated by visualization that the learned features through STA-CRNN are in line with clinical judgement. STA-CRNN provides a promising method for automatic arrhythmia detection, which has a potential to assist cardiologists in the diagnosis of arrhythmias.

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal.

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals

Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However, visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.

AUTOMATIC ARRHYTHMIA DETECTION ALGORITHM USING STATISTICAL AND AUTOREGRESSIVE MODEL FEATURES

Human heart healthiness is one of the major components to determine a person’s overall healthiness. Automatic arrhythmia detection is important in a remote area where there is a lack of experienced cardiologists. In this work, an automatic arrhythmia detection algorithm is developed using statistical and autoregressive features to assist medical officers in the diagnosis of arrhythmia diseases. Basic statistical components namely mean, energy, standard deviation, mean absolute deviation, fractal, inter-quartile range and min/max value, were calculated. Alongside with statistical features, 10th order auto-regressive model parameters are used as input features to support vector machine (SVM). All features are calculated using an electrocardiogram (ECG) signals windowed into per beat manner. The proposed algorithm is able to classify normal ECG beat and five types of abnormal ECG beat; paced beat, right & left bundle branch block beat, premature ventricular contraction beat and aberrated atrial premature beat. By using SVM with quadratic and cubic kernel function, the proposed algorithm achieved the best accuracy of 95.8%.
 Keywords: ECG, cascade-SVM, AR model, statistical model, heart condition, computer-aided diagnosis

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.

A high-precision arrhythmia classification method based on dual fully connected neural network

As an important arrhythmia detection method, the electrocardiogram (ECG) can directly reflect abnormalities in cardiac physiological activity. In view of the difficulty in the diagnosis of arrhythmia in different people, automatic arrhythmia detection methods have been studied in previous works. In this paper, we present a dual fully-connected neural network model for accurate classification of heartbeats. Our method is following the AAMI inter-patient standard, which includes normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). Firstly, a total of 105 features are extracted from the preprocessed signals. Then, a two-layer classifier is introduced in the classification stage. Each layer contains two independent fully-connected neural networks, and the threshold criterion is also added in the second layer. For verification, both the MIT arrhythmia database (MITDB) and the MIT supraventricular arrhythmia database (SVDB) were adopted. The experiments demonstrate that the proposed method has high performance for arrhythmia detection. It also achieves high sensitivity for class S and V, which can easily detect potentially abnormal heartbeats. Furthermore, the proposed method can interfere with the classification effect for a certain disease and have more advantages in dataset size when comparing a convolutional neural network (CNN). Once properly trained, the proposed method can be employed as a tool to automatically detect arrhythmia from ECG.

Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals

Electrocardiography (ECG) is widely used for arrhythmia detection nowadays. The machine learning methods with signal processing algorithms have been used for automated diagnosis of cardiac health using ECG signals. In this article, discrete wavelet transform (DWT) coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection. The ECG signals of 10 s duration are subjected to DWT to decompose up to five levels. The 1D-HLP extracts 512 dimensional features from each level of the five levels of low pass filter. Then, these extracted features are concatenated to obtain 512×6=3072 dimensional feature set. These fused features are subjected to neighborhood component analysis (NCA) feature reduction technique to obtain 64, 128 and 256 features. Finally, these features are subjected to 1 nearest neighborhood (1NN) classifier for classification with 4 distance metrics namely city block, Euclidean, spearman and cosine. We have obtained a classification accuracy of 95.0% in classifying 17 arrhythmia classes using MIT-BIH Arrhythmia ECG dataset. Our results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmia detection using ECG signals.

Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network

Automatic arrhythmia detection from Electrocardiogram (ECG) plays an important role in early prevention and diagnosis of cardiovascular diseases. Convolutional neural network (CNN) is a simpler, more noise-immune solution than traditional methods in multi-class arrhythmia classification. However, suffering from lack of consideration for temporal feature of ECG signal, CNN couldn’t accept varied-length ECG signal and had limited performance in detecting paroxysmal arrhythmias. To address these issues, we proposed attention-based time-incremental convolutional neural network (ATI-CNN), a deep neural network model achieving both spatial and temporal fusion of information from ECG signals by integrating CNN, recurrent cells and attention module. Comparing to CNN model, this model features flexible input length, halved parameter amount as well as more than 90% computation reduction in real-time processing. The experiment result shows that, ATI-CNN reached an overall classification accuracy of 81.2%. In comparison with a classical 16-layer CNN named VGGNet, ATI-CNN achieved accuracy increases of 7.7% in average and up to 26.8% in detecting paroxysmal arrhythmias. Combining all these excellent features, ATI-CNN offered an exemplification for all kinds of varied-length signal processing problems.

Tailoring optogenetic tools for AF treatment.

A novel bioelectronic system that combines automated arrhythmia detection with optogenetic engineering for arrhythmia termination is able to stop atrial fibrillation without the need of electrical shock in a closed-chest experimental model.

Diagnostic and therapeutic value of implantable loop recorder: A tertiary care center experience.

Implantable loop recorders (ILRs) are effective in achieving symptom-rhythm correlation. However, diagnostic yield in routine clinical practice is not well established. Patients undergoing ILR implantation between April 2010 and May 2015 were included. All devices were enrolled in remote monitoring with automatic arrhythmia detection and P sense algorithms switched "ON." Symptom-rhythm correlation was assessed and changes in management were recorded. A total of 312 patients (57% male, age 53±22 years; median CHADS2VaSc score=1) were included in this study. ILRs were implanted for evaluation of syncope in 206 (66.0%), presyncope in 23 (7.4%), unexplained palpitations in 51 (16.3%), and cryptogenic stroke in 27 (8.7%) patients. ILR monitoring yielded a diagnosis that changed management strategy in 146 (46.8%) patients over a median of 12 (1-42) months. Out of 163 (52.2%) patients with symptoms during the monitoring period, 100 (61.3%) had an arrhythmia. ILR was useful in ruling out an arrhythmic cause for symptoms in 63 (38.7%) patients. ILR results led to pacemaker implantation in 23 patients (7.4% overall and 11.2% of those with syncope) after median follow-up of 3 months. A new diagnosis of atrial fibrillation was made in 38 (12.2%) patients, 11 of whom were initiated on oral anticoagulants. ILR results led to pacemaker implantation in 31 patients (9.9% overall and 19.0% of those with syncope) after median follow-up of 3 months. A new diagnosis of atrial fibrillation was made in 38 (12.2%) patients, nine of whom were initiated on oral anticoagulants. Overall, ILR led to a change in management in 47% patients with a number needed to implant of 2.1 to change management. ILR monitoring is effective in achieving symptom-rhythm correlation and results in changes in management in nearly half of implanted patients. Additional studies are needed to evaluate cost efficacy of ILR and the optimal monitoring duration.