Background Electrocardiogram(ECG)analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders.Given the close connection between cardiovascular and neurocognitive health...Background Electrocardiogram(ECG)analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders.Given the close connection between cardiovascular and neurocognitive health,ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions.This highlights the potential of ECG as a biomarker to improve detection,therapy monitoring and risk stratification in patients with neurocognitive disorders,an area that remains underexplored.Aims We aimed to demonstrate the feasibility of predicting neurocognitive disorders from ECG features across diverse patient populations.Methods ECG features and demographic data were used to predict neurocognitive disorders,as defined by the International Classification of Diseases 10th revision,focusing on dementia,delirium and Parkinson's disease.Internal and external validations were performed using the Medical Information Mart for Intensive CareⅣand ECG-View datasets.Predictive performance was assessed by the area under the receiver operating characteristic curve(AUROC)scores,and Shapley values were used to interpret feature contributions.Results Significant predictive performance was observed for several neurocognitive disorders.The highest predictive performance was observed for F03:dementia,with an internal AUROC of 0.848(95%confidence interval(CI)0.848 to 0.848)and an external AUROC of 0.865(95%CI 0.864 to 0.965),followed by G30:Alzheimer's disease,with an internal AUROC of 0.809(95%CI 0.808 to 0.810)and an external AUROC of 0.863(95%CI 0.863 to 0.864).Feature importance analysis revealed both established and novel ECG correlates.Conclusions These findings suggest that ECG holds promise as a non-invasive,explainable biomarker for selected neurocognitive disorders.This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications,including early detection and personalised monitoring.展开更多
心电信号容易受到采集设备和被测者状态的干扰,为此提出一种归一化最小均方差(Normalized Least Mean Square,NLMS)和自适应噪声完备集合模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)组合的...心电信号容易受到采集设备和被测者状态的干扰,为此提出一种归一化最小均方差(Normalized Least Mean Square,NLMS)和自适应噪声完备集合模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)组合的去噪方法。其中:优化的NLMS算法通过简化步长因子和输入信号的关系减少运算量,并结合迭代次数对步长因子进行优化,提高算法收敛性能;改进的CEEMDAN算法结合高斯白噪声的统计特性对所有IMF分量进行显著性检验,来识别和筛选含有噪声的成分,使干净信号与噪声信号分离。实验结果表明,在不同噪声强度下,该方法相比于CEEMDAN直接去噪效果更佳,且缓解了传统NLMS收敛速度与运算量之间的矛盾。展开更多
Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Researc...Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Research indicates that emergency department(ED)clinician interruptions occur every 4-10 min,which is significantly more common than in other specialties.[5]This increases the cognitive load and error rates and impacts patient care and clinical effi ciency.[1,2,5]De-prioritization protocols have been introduced in certain centers in the United Kingdom(UK),removing the need for clinician ECG interpretation where ECGs have been interpreted as normal by the machine.展开更多
The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardio...The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.展开更多
Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advanc...Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.展开更多
Arrhythmias stand out for having irregular cardiac rhythms,and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes.Despite the progress in t...Arrhythmias stand out for having irregular cardiac rhythms,and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes.Despite the progress in this field,existing research efforts have encountered limitations,necessitating innovative approaches to address diagnostic challenges effectively.The primary objective of this research is to propose an innovative classification methodology for distinguishing five distinct arrhythmia classes:atrial premature beat(A),normal(N),ventricular premature beat(V),right bundle branch block(R),and left bundle branch block(L).The proposed methodology involves constructing a hybrid model that incorporates an attention mechanism,utilizing electrocardiogram(ECG)data from an open-source repository.Additionally,we have incorporated an explainability feature into the model,allowing for the interpretation and explanation of its predictions.This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics.Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance.Findings from this study underscore the superiority of the proposed classification model over existing methodologies.Quantitative analysis demonstrates its outstanding performance.The approach,outperforming existing methods,achieves high levels of accuracy(99.16%),specificity(99.79%),recall(99.20%),precision(99.20%),F1-measure(99.16%),and AUC(99.92%).This research advances medical diagnostics by integrating advanced machine-learning techniques to enhance arrhythmia detection.展开更多
文摘Background Electrocardiogram(ECG)analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders.Given the close connection between cardiovascular and neurocognitive health,ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions.This highlights the potential of ECG as a biomarker to improve detection,therapy monitoring and risk stratification in patients with neurocognitive disorders,an area that remains underexplored.Aims We aimed to demonstrate the feasibility of predicting neurocognitive disorders from ECG features across diverse patient populations.Methods ECG features and demographic data were used to predict neurocognitive disorders,as defined by the International Classification of Diseases 10th revision,focusing on dementia,delirium and Parkinson's disease.Internal and external validations were performed using the Medical Information Mart for Intensive CareⅣand ECG-View datasets.Predictive performance was assessed by the area under the receiver operating characteristic curve(AUROC)scores,and Shapley values were used to interpret feature contributions.Results Significant predictive performance was observed for several neurocognitive disorders.The highest predictive performance was observed for F03:dementia,with an internal AUROC of 0.848(95%confidence interval(CI)0.848 to 0.848)and an external AUROC of 0.865(95%CI 0.864 to 0.965),followed by G30:Alzheimer's disease,with an internal AUROC of 0.809(95%CI 0.808 to 0.810)and an external AUROC of 0.863(95%CI 0.863 to 0.864).Feature importance analysis revealed both established and novel ECG correlates.Conclusions These findings suggest that ECG holds promise as a non-invasive,explainable biomarker for selected neurocognitive disorders.This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications,including early detection and personalised monitoring.
文摘心电信号容易受到采集设备和被测者状态的干扰,为此提出一种归一化最小均方差(Normalized Least Mean Square,NLMS)和自适应噪声完备集合模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)组合的去噪方法。其中:优化的NLMS算法通过简化步长因子和输入信号的关系减少运算量,并结合迭代次数对步长因子进行优化,提高算法收敛性能;改进的CEEMDAN算法结合高斯白噪声的统计特性对所有IMF分量进行显著性检验,来识别和筛选含有噪声的成分,使干净信号与噪声信号分离。实验结果表明,在不同噪声强度下,该方法相比于CEEMDAN直接去噪效果更佳,且缓解了传统NLMS收敛速度与运算量之间的矛盾。
文摘Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Research indicates that emergency department(ED)clinician interruptions occur every 4-10 min,which is significantly more common than in other specialties.[5]This increases the cognitive load and error rates and impacts patient care and clinical effi ciency.[1,2,5]De-prioritization protocols have been introduced in certain centers in the United Kingdom(UK),removing the need for clinician ECG interpretation where ECGs have been interpreted as normal by the machine.
基金funded by Researchers Supporting ProjectNumber(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.
文摘Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.
基金supported by the National Natural Science Foundation of China under Grant No.62271127the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China and the West China Hospital of Sichuan University under Grants No.ZYGX2022YGRH011 and No.HXDZ22005+1 种基金the Natural Science Foundation of Sichuan,China under Grant No.23NSFSC0627Sichuan Provincial Key Laboratory Fund for Ultra Sound Cardioelectrophysiology and Biomechanics,China under Grant No.2023KFKT01.
文摘Arrhythmias stand out for having irregular cardiac rhythms,and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes.Despite the progress in this field,existing research efforts have encountered limitations,necessitating innovative approaches to address diagnostic challenges effectively.The primary objective of this research is to propose an innovative classification methodology for distinguishing five distinct arrhythmia classes:atrial premature beat(A),normal(N),ventricular premature beat(V),right bundle branch block(R),and left bundle branch block(L).The proposed methodology involves constructing a hybrid model that incorporates an attention mechanism,utilizing electrocardiogram(ECG)data from an open-source repository.Additionally,we have incorporated an explainability feature into the model,allowing for the interpretation and explanation of its predictions.This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics.Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance.Findings from this study underscore the superiority of the proposed classification model over existing methodologies.Quantitative analysis demonstrates its outstanding performance.The approach,outperforming existing methods,achieves high levels of accuracy(99.16%),specificity(99.79%),recall(99.20%),precision(99.20%),F1-measure(99.16%),and AUC(99.92%).This research advances medical diagnostics by integrating advanced machine-learning techniques to enhance arrhythmia detection.
