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
Electrocardiogram(ECG)monitoring is used to diagnose cardiovascular diseases,for which wearable electronics have attracted much attention due to their lightweight,comfort,and long-term use.This study developed a weara...Electrocardiogram(ECG)monitoring is used to diagnose cardiovascular diseases,for which wearable electronics have attracted much attention due to their lightweight,comfort,and long-term use.This study developed a wearablemultilead ECG sensing system with on-skin stretchable and conductive silver(Ag)-coated fiber/silicone(AgCF-S)dry adhesives.Tangential and normal adhesion to pigskin(0.43 and 0.20 N/cm2,respectively)was optimized by the active control of fiber density and mixing ratio,resulting in close contact in the electrode–skin interface.The breathableAgCF-S dry electrodewas nonallergenic after continuous fit for 24 h and can be reused/cleaned(>100 times)without loss of adhesion.The AgCF encapsulated inside silicone elastomers was overlapped to construct a dynamic network under repeated stretching(10%strain)and bending(90°)deformations,enabling small intrinsic impedance(0.3,0.1 Hz)and contact impedance variation(0.7 k)in high-frequency vibration(70 Hz).All hard/soft modules of the multilead ECG system were integrated into lightweight clothing and equipped with wireless transmission for signal visualization.By synchronous acquisition of I–III,aVR,aVL,aVF,and V4 lead data,the multilead ECG sensing system was suitable for various scenarios,such as exercise,rest,and sleep,with extremely high signal-to-noise ratios.展开更多
From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each other.Therefore,all these leads report different aspects of an arrhythmia.Their difference...From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each other.Therefore,all these leads report different aspects of an arrhythmia.Their differences lie in the level of highlighting and displaying information about that arrhythmia.For example,although all leads show traces of atrial excitation,this function is more evident in lead II than in any other lead.In this article,a new model was proposed using ECG functional and structural dependencies between heart leads.In the prescreening stage,the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed manner.The mutual information indices were used to assess the relationship between leads.In order to calculate mutual information,the correlation between the 12 ECG leads has been calculated.The output of this step is a matrix containing all mutual information.Furthermore,to calculate the structural information of ECG signals,a capsule neural network was implemented to aid physicians in the automatic classification of cardiac arrhythmias.The architecture of this capsule neural network has been modified to perform the classification task.In the experimental results section,the proposed model was used to classify arrhythmias in ECG signals from the Chapman dataset.Numerical evaluations showed that this model has a precision of 97.02%,recall of 96.13%,F1-score of 96.57%and accuracy of 97.38%,indicating acceptable performance compared to other state-of-the-art methods.The proposed method shows an average accuracy of 2%superiority over similar works.展开更多
Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affec...Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.展开更多
Background: A major cause of mortality and disability on a global scale is myocardial infarction (MI). These days, the most reliable way to detect and measure MI is via cardiovascular magnetic resonance imaging (CMR)....Background: A major cause of mortality and disability on a global scale is myocardial infarction (MI). These days, the most reliable way to detect and measure MI is via cardiovascular magnetic resonance imaging (CMR). Aims and Objectives: To evaluate the effectiveness of the Electrocardiogram DETERMINE Score in predicting the severity of coronary artery disease (CAD) in patients who have experienced an Acute Myocardial Infarction (AMI) & to assess improvements in left ventricular function at 6 months following coronary artery bypass grafting (CABG). Subjects and Methods: This Observational cohort study was done at the Cardiology and Radiology department and cardiac surgery department, Al-Azhar university hospitals and Helwan University hospital. The study involved 700 cases who patients diagnosed with Acute Myocardial Infarction and fulfilled specific criteria for selection. Result: There was highly statistically significant relation between Myocardial infarction size and ECG Marker Score as mean infarct size elevated When the number of ECG markers increased. There was a highly statistically significant relation between myocardial infarct segments, myocardial infarction size and improvement of cardiac function 6 months post-CABG. Conclusion: The study found that larger myocardial infarctions corresponded with higher DETERMINE Scores. It concluded that an ECG-based score better estimates infarct size than LVEF alone. Additionally, there was a significant statistical correlation between the size and segmentation of myocardial infarction and better cardiac function six months after CABG.展开更多
Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,an...Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.展开更多
The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnos...The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.展开更多
Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenou...Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenous H2S on the electrocardiogram （ECG） of rats generally anesthetized by zoletil, different doses of NariS solution were used for the intervention of intraperitoneal injection 20 rain before the zoletil anesthesia. The ECGs of rats from each treatment group during the time range of 10^th-50^th min were determined under general anesthesia, and then were compared with those from the control group. The results showed that exogenous H2S could significantly reduce the Q-T interval time limit, thus played a role in slowing tachycardia or arrhythmia and other anomalies, thereby protecting the heart. S-T segment and T segment evaluation values were significantly reduced, which might be associated with bradycardia.展开更多
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supported by the Natural Science Foundation of Guangdong Province,China(No.2021B1515020087)the National Natural Science Foundation of China(No.51905178)the Climbing Program Foundation of Guangdong Province(No.pdjh2022a0024).
文摘Electrocardiogram(ECG)monitoring is used to diagnose cardiovascular diseases,for which wearable electronics have attracted much attention due to their lightweight,comfort,and long-term use.This study developed a wearablemultilead ECG sensing system with on-skin stretchable and conductive silver(Ag)-coated fiber/silicone(AgCF-S)dry adhesives.Tangential and normal adhesion to pigskin(0.43 and 0.20 N/cm2,respectively)was optimized by the active control of fiber density and mixing ratio,resulting in close contact in the electrode–skin interface.The breathableAgCF-S dry electrodewas nonallergenic after continuous fit for 24 h and can be reused/cleaned(>100 times)without loss of adhesion.The AgCF encapsulated inside silicone elastomers was overlapped to construct a dynamic network under repeated stretching(10%strain)and bending(90°)deformations,enabling small intrinsic impedance(0.3,0.1 Hz)and contact impedance variation(0.7 k)in high-frequency vibration(70 Hz).All hard/soft modules of the multilead ECG system were integrated into lightweight clothing and equipped with wireless transmission for signal visualization.By synchronous acquisition of I–III,aVR,aVL,aVF,and V4 lead data,the multilead ECG sensing system was suitable for various scenarios,such as exercise,rest,and sleep,with extremely high signal-to-noise ratios.
文摘From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each other.Therefore,all these leads report different aspects of an arrhythmia.Their differences lie in the level of highlighting and displaying information about that arrhythmia.For example,although all leads show traces of atrial excitation,this function is more evident in lead II than in any other lead.In this article,a new model was proposed using ECG functional and structural dependencies between heart leads.In the prescreening stage,the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed manner.The mutual information indices were used to assess the relationship between leads.In order to calculate mutual information,the correlation between the 12 ECG leads has been calculated.The output of this step is a matrix containing all mutual information.Furthermore,to calculate the structural information of ECG signals,a capsule neural network was implemented to aid physicians in the automatic classification of cardiac arrhythmias.The architecture of this capsule neural network has been modified to perform the classification task.In the experimental results section,the proposed model was used to classify arrhythmias in ECG signals from the Chapman dataset.Numerical evaluations showed that this model has a precision of 97.02%,recall of 96.13%,F1-score of 96.57%and accuracy of 97.38%,indicating acceptable performance compared to other state-of-the-art methods.The proposed method shows an average accuracy of 2%superiority over similar works.
基金the National Natural Science Foundation of China under Grant 62172059 and 62072055Hunan Provincial Natural Science Foundations of China under Grant 2022JJ50318 and 2022JJ30621Scientific Research Fund of Hunan Provincial Education Department of China under Grant 22A0200 and 20K098。
文摘Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.
文摘Background: A major cause of mortality and disability on a global scale is myocardial infarction (MI). These days, the most reliable way to detect and measure MI is via cardiovascular magnetic resonance imaging (CMR). Aims and Objectives: To evaluate the effectiveness of the Electrocardiogram DETERMINE Score in predicting the severity of coronary artery disease (CAD) in patients who have experienced an Acute Myocardial Infarction (AMI) & to assess improvements in left ventricular function at 6 months following coronary artery bypass grafting (CABG). Subjects and Methods: This Observational cohort study was done at the Cardiology and Radiology department and cardiac surgery department, Al-Azhar university hospitals and Helwan University hospital. The study involved 700 cases who patients diagnosed with Acute Myocardial Infarction and fulfilled specific criteria for selection. Result: There was highly statistically significant relation between Myocardial infarction size and ECG Marker Score as mean infarct size elevated When the number of ECG markers increased. There was a highly statistically significant relation between myocardial infarct segments, myocardial infarction size and improvement of cardiac function 6 months post-CABG. Conclusion: The study found that larger myocardial infarctions corresponded with higher DETERMINE Scores. It concluded that an ECG-based score better estimates infarct size than LVEF alone. Additionally, there was a significant statistical correlation between the size and segmentation of myocardial infarction and better cardiac function six months after CABG.
文摘Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.
文摘The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.
文摘Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenous H2S on the electrocardiogram （ECG） of rats generally anesthetized by zoletil, different doses of NariS solution were used for the intervention of intraperitoneal injection 20 rain before the zoletil anesthesia. The ECGs of rats from each treatment group during the time range of 10^th-50^th min were determined under general anesthesia, and then were compared with those from the control group. The results showed that exogenous H2S could significantly reduce the Q-T interval time limit, thus played a role in slowing tachycardia or arrhythmia and other anomalies, thereby protecting the heart. S-T segment and T segment evaluation values were significantly reduced, which might be associated with bradycardia.
