The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract ...The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.展开更多
This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for ar...This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.展开更多
Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this...Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this process would aid the diagnosis by providing fast,costefficient,and accurate solutions at scale.This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography(ECG)signals causing arrhythmia.In this era of applied intelligence,automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions.In this research,our contributions are two-fold.Firstly,the Dual-Tree Complex Wavelet Transform(DT-CWT)method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features.Next,A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters.To ensure that the model’s generalizability,a set of five traintest variants are implied.The proposed model attains the highest accuracy of 98.5%for classifying 8 variants of arrhythmia on the MIT-BIH dataset.To test the resilience of the model,the unseen(test)samples are increased by 5x and the deviation in accuracy score and MSE was 0.12%and 0.1%respectively.Further,to assess the diagnostic model performance,AUC-ROC curves are plotted.At every test level,the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application.As a note,this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.展开更多
Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease,and it is a challenging task as it requires identifying the label subset most related to each instance.In this...Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease,and it is a challenging task as it requires identifying the label subset most related to each instance.In this paper,by integrating a deep residual neural network and auto-encoder,we propose an advanced deep neural network(DNN)framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias.Firstly,a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms(ECGs).Secondly,the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data,and then to achieve unified feature-label embedding.Thirdly,the label-correlation aware loss is introduced to optimize the auto-encoder architecture,which enables our model to exploit labelcorrelation for improved multi-label prediction.Our proposed DNN model can allow end-to-end training and prediction,which can perform feature-aware,label embedding,and label-correlation aware prediction in a unified framework.Finally,our proposed model is evaluated on the currently largest public dataset worldwide,and achieves the challenge metric scores of 0.492,0.495,and 0.490 on the 12-lead,3-lead,and all-lead version ECGs,respectively.The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting,which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.展开更多
This paper proposes a novel arrhythmia classification method combining Convolutional Neural Networks(CNN),Attention mechanisms,and Bidirectional Transformers(BiTransformer).The method aims to improve the accuracy and ...This paper proposes a novel arrhythmia classification method combining Convolutional Neural Networks(CNN),Attention mechanisms,and Bidirectional Transformers(BiTransformer).The method aims to improve the accuracy and robustness of arrhythmia detection in electrocardiogram(ECG)signals.Initially,the CNN module extracts local spatial features from raw ECG data,effectively capturing the morphological characteristics of different arrhythmia types.Subsequently,the Attention mechanism is applied to weigh the importance of different segments in the ECG signal,allowing the model to focus on critical features that are most indicative of arrhythmia.Finally,the BiTransformer module processes the feature sequences bidirectionally,capturing both forward and backward dependencies in the ECG signal.This comprehensive approach enables the model to integrate local and global information,enhancing its ability to classify various arrhythmias accurately.Experiments conducted on the MIT-BIH Arrhythmia Database demonstrate that the proposed method achieves state-of-the-art performance,with a significant improvement in classification accuracy compared to traditional methods.The results highlight the effectiveness of combining CNN,Attention,and BiTransformer for arrhythmia classification,offering a promising direction for automated ECG analysis and clinical applications.展开更多
The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehe...The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehensive review of Transformer-based arrhythmia classification methods,examining their evolution,current capabilities,and future potential.We systematically analyze the architectural adaptations of Transformers for ECG signal processing,including Vision Transformers adapted for 1D medical signals,hybrid CNN-Transformer models,and lightweight implementations for edge computing.Our review encompasses recent studies demonstrating exceptional performance,with models like ECGformer achieving 98%accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97%accuracy with only 6k parameters suitable for wearable devices.We discuss key advantages including the ability to capture long-range dependencies in ECG sequences,handle variable-length inputs,and integrate multi-lead spatial information through attention mechanisms.However,significant challenges remain,including high computational requirements,dependence on large labeled datasets,limited interpretability in clinical settings,and over-fitting r isks with imbalanced data.The paper explores emerging solutions such as transfer learning,data augmentation techniques,and explainable AI methods to address these limitations.Future prospects include the development of more efficient architectures for real-time monitoring,integration with multi-modal physiological data,and enhanced clinical interpretability.This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate,efficient,and clinically viable automated arrhythmia detection systems.展开更多
In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine （SVM） classifier. The method extracts electrocardiogram＇s spectral and three tim...In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine （SVM） classifier. The method extracts electrocardiogram＇s spectral and three timing inter- val features. Non-parametric power spectral density （PSD） estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using parti- cle swarm optimization （PSO）. These parameters are： Gaus- sian radial basis function （GRBF） kernel parameter o- and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid par- ticle swarm optimization-support vector machine （SVMPSO） classification method offers significantly improved perfor- mance over the SVM which has constant and manually ex- tracted parameter.展开更多
文摘The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.
文摘This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.
基金This research was partially supported by JNTU Hyderabad,India under Grant proceeding number:JNTUH/TEQIP-III/CRS/2019/CSE/08.The authors are grateful for the support provided by the TEQIP-III team.
文摘Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this process would aid the diagnosis by providing fast,costefficient,and accurate solutions at scale.This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography(ECG)signals causing arrhythmia.In this era of applied intelligence,automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions.In this research,our contributions are two-fold.Firstly,the Dual-Tree Complex Wavelet Transform(DT-CWT)method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features.Next,A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters.To ensure that the model’s generalizability,a set of five traintest variants are implied.The proposed model attains the highest accuracy of 98.5%for classifying 8 variants of arrhythmia on the MIT-BIH dataset.To test the resilience of the model,the unseen(test)samples are increased by 5x and the deviation in accuracy score and MSE was 0.12%and 0.1%respectively.Further,to assess the diagnostic model performance,AUC-ROC curves are plotted.At every test level,the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application.As a note,this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.
基金supported by the National Natural Science Foundation of China(Nos.U21A20447 and 62331025)National Key Research and Development Program of China(No.2021YFC3002204)+4 种基金Pilots Precise Selection and Performance Improvement Special Project(No.2019ZTB02)Special Equipment Scientific Research Key Project(No.LB2020LA060003)CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-019)Scientific Research Foundation Project Funded by Education Department of Yunnan Province(No.2024J0134)Yunnan Fundamental Research Projects(No.202401CF070028).
文摘Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease,and it is a challenging task as it requires identifying the label subset most related to each instance.In this paper,by integrating a deep residual neural network and auto-encoder,we propose an advanced deep neural network(DNN)framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias.Firstly,a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms(ECGs).Secondly,the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data,and then to achieve unified feature-label embedding.Thirdly,the label-correlation aware loss is introduced to optimize the auto-encoder architecture,which enables our model to exploit labelcorrelation for improved multi-label prediction.Our proposed DNN model can allow end-to-end training and prediction,which can perform feature-aware,label embedding,and label-correlation aware prediction in a unified framework.Finally,our proposed model is evaluated on the currently largest public dataset worldwide,and achieves the challenge metric scores of 0.492,0.495,and 0.490 on the 12-lead,3-lead,and all-lead version ECGs,respectively.The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting,which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.
基金supported by the Henan Provincial Scientific and Technological Research Project(No.252102210005,222102310222)the Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province(No.2025GGJS149).
文摘This paper proposes a novel arrhythmia classification method combining Convolutional Neural Networks(CNN),Attention mechanisms,and Bidirectional Transformers(BiTransformer).The method aims to improve the accuracy and robustness of arrhythmia detection in electrocardiogram(ECG)signals.Initially,the CNN module extracts local spatial features from raw ECG data,effectively capturing the morphological characteristics of different arrhythmia types.Subsequently,the Attention mechanism is applied to weigh the importance of different segments in the ECG signal,allowing the model to focus on critical features that are most indicative of arrhythmia.Finally,the BiTransformer module processes the feature sequences bidirectionally,capturing both forward and backward dependencies in the ECG signal.This comprehensive approach enables the model to integrate local and global information,enhancing its ability to classify various arrhythmias accurately.Experiments conducted on the MIT-BIH Arrhythmia Database demonstrate that the proposed method achieves state-of-the-art performance,with a significant improvement in classification accuracy compared to traditional methods.The results highlight the effectiveness of combining CNN,Attention,and BiTransformer for arrhythmia classification,offering a promising direction for automated ECG analysis and clinical applications.
基金supported by the Henan Provincial Scientific and Technological Research Project(No.252102210005,222102310222)the Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province(No.2025GGJS149).
文摘The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehensive review of Transformer-based arrhythmia classification methods,examining their evolution,current capabilities,and future potential.We systematically analyze the architectural adaptations of Transformers for ECG signal processing,including Vision Transformers adapted for 1D medical signals,hybrid CNN-Transformer models,and lightweight implementations for edge computing.Our review encompasses recent studies demonstrating exceptional performance,with models like ECGformer achieving 98%accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97%accuracy with only 6k parameters suitable for wearable devices.We discuss key advantages including the ability to capture long-range dependencies in ECG sequences,handle variable-length inputs,and integrate multi-lead spatial information through attention mechanisms.However,significant challenges remain,including high computational requirements,dependence on large labeled datasets,limited interpretability in clinical settings,and over-fitting r isks with imbalanced data.The paper explores emerging solutions such as transfer learning,data augmentation techniques,and explainable AI methods to address these limitations.Future prospects include the development of more efficient architectures for real-time monitoring,integration with multi-modal physiological data,and enhanced clinical interpretability.This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate,efficient,and clinically viable automated arrhythmia detection systems.
文摘In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine （SVM） classifier. The method extracts electrocardiogram＇s spectral and three timing inter- val features. Non-parametric power spectral density （PSD） estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using parti- cle swarm optimization （PSO）. These parameters are： Gaus- sian radial basis function （GRBF） kernel parameter o- and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid par- ticle swarm optimization-support vector machine （SVMPSO） classification method offers significantly improved perfor- mance over the SVM which has constant and manually ex- tracted parameter.
