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
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
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
Aslanger’s sign,also known as the arterial pulse tapping artifact or electromechanical association artifact,is an electrocardiographic artifact caused by arterial pulsation at the site where the limb leads of the sta...Aslanger’s sign,also known as the arterial pulse tapping artifact or electromechanical association artifact,is an electrocardiographic artifact caused by arterial pulsation at the site where the limb leads of the standard 12-lead electrocardiogram near the radial or posterior tibial arteries are positioned,particularly in hyperdynamic states.[1–8]It occurs in every cardiac cycle with a constant coupling interval between the QRS complex and artifact.This synchronization with the underlying heart rhythm makes it less likely to be recognized as an artifact compared to unsynchronized artifacts,such as those caused by limb movement and inadequate contact between the electrode and skin.[1,2,7,8]Almost all reported cases of Aslanger’s sign exhibit an unusual waveform morphology in all 12 leads except one of the standard 12-lead electrocardiogram.This sign is often confused with an electrocardiographic finding commonly observed during acute coronary events.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.
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Aslanger’s sign,also known as the arterial pulse tapping artifact or electromechanical association artifact,is an electrocardiographic artifact caused by arterial pulsation at the site where the limb leads of the standard 12-lead electrocardiogram near the radial or posterior tibial arteries are positioned,particularly in hyperdynamic states.[1–8]It occurs in every cardiac cycle with a constant coupling interval between the QRS complex and artifact.This synchronization with the underlying heart rhythm makes it less likely to be recognized as an artifact compared to unsynchronized artifacts,such as those caused by limb movement and inadequate contact between the electrode and skin.[1,2,7,8]Almost all reported cases of Aslanger’s sign exhibit an unusual waveform morphology in all 12 leads except one of the standard 12-lead electrocardiogram.This sign is often confused with an electrocardiographic finding commonly observed during acute coronary events.
