Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-s...Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.展开更多
This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart ar...This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart arrhythmias.The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency.The model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals.The integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia classification.Evaluated on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.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
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
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
Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identif...Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identifying arrhythmias.The evolving landscape of fabric electrodes,specifically designed for the prolonged monitoring of human ECG signals,is the focus of this research.Adhering to the preferred reporting items for systematic reviews and meta-analyses(PRISMA)statement and assimilating data from 81 pertinent studies sourced from reputable databases,the research conducts a comprehensive systematic review and meta-analysis on the materials,fabric structures and preparation methods of fabric electrodes in the existing literature.It provides a nuanced assessment of the advantages and disadvantages of diverse textile materials and structures,elucidating their impacts on the stability of biomonitoring signals.Furthermore,the study outlines current developmental constraints and future trajectories for fabric electrodes.These insights could serve as essential guidance for ECG monitoring system designers,aiding them in the selection of materials that optimize the measurement of biopotential signals.展开更多
Atrial fibrillation (AF) is the most common chronic arrhythmia in clinical practice, which can cause high disability and mortality with the progress of the disease. Many studies at home and abroad have shown that the ...Atrial fibrillation (AF) is the most common chronic arrhythmia in clinical practice, which can cause high disability and mortality with the progress of the disease. Many studies at home and abroad have shown that the incidence of atrial fibrillation gradually increases with age. Clinically, the onset of most AF patients is insidious, which is difficult to capture by routine electrocardiogram, and there is some difficulty in the diagnosis. In order to make the early diagnosis of atrial fibrillation more efficient and accurate, this paper reviews the current status and research progress of detection technology for atrial fibrillation at home and abroad, in order to provide a scientific basis for the early diagnosis of atrial fibrillation.展开更多
The two most frequent causes of paroxysmal SVT are atrioventricular tachycardia (AVRT) and atrioventricular nodal re-entrant tachycardia (AVNRT). The purpose of this study was to assess the diagnostic efficacy of trad...The two most frequent causes of paroxysmal SVT are atrioventricular tachycardia (AVRT) and atrioventricular nodal re-entrant tachycardia (AVNRT). The purpose of this study was to assess the diagnostic efficacy of traditional and newly proposed ECG criteria in the identification of Avnrt and Avrt. Aim of the Study: The aim of this study was to evaluate Atrioventricular Nodal Reentrant Tachycardia (AVNRT) and Atrioventricular Re-entrant Tachycardia (AVRT) using both traditional and novel criteria. Methods: This prospective observational study was conducted at the Electrophysiology Unit, Department of Cardiology, National Institute of Cardiovascular Diseases (NICVD) in Dhaka, from February 2019 to January 2020. A total of 62 patients with Supraventricular Tachycardia (SVT) undergoing electrophysiology study (EPS) were included. Standard ECG criteria were applied for the differential diagnosis, and electrophysiological diagnoses were made using established criteria. Statistical analysis, including descriptive statistics and appropriate tests, was performed using SPSS 23.0. Result: In our study of 62 patients with Supraventricular Tachycardia (SVT), we found that 66.1% had AVNRT and 33.9% had AVRT. The mean age in AVNRT was higher than AVRT (41.3 ± 9.7 vs. 38.5 ± 14.3, p = 0.36) with statistically no significant difference, with similar gender distribution between AVNRT and AVRT groups. Classical AVNRT criteria were present in 30.6% of patients, and 45.2% showed a Pseudo R' wave in aVR. Additionally, 30.6% had an RP interval ≥100ms, more prevalent in AVRT patients (66.7%). Conclusion: Integrating traditional and novel criteria, including lead aVR analysis, enhances the electrocardiographic diagnosis of AVNRT and AVRT, offering a pathway to refined patient care.展开更多
Objective:To investigate and thoroughly understand the physical examination results of retired employees from a certain unit in Beijing,analyze their bone mineral density(BMD),and identify risk factors that may indica...Objective:To investigate and thoroughly understand the physical examination results of retired employees from a certain unit in Beijing,analyze their bone mineral density(BMD),and identify risk factors that may indicate osteoporosis.This provides a reference for the individualized prevention,identification,and control of osteoporosis among retired employees.Methods:The bone mineral density and potential factors of 148 retired employees from a unit in 2023 were analyzed and categorized into osteoporosis and non-osteoporosis groups.Key factors from the physical examinations of the two groups were compared.Spearman’s correlation analysis was used to determine the correlation between key factors and osteoporosis.Significant key factors were included in a regression analysis.A multivariate binary logistics regression was employed to identify risk factors indicative of osteoporosis.Results:Correlation analysis revealed that gender,age,and ECG ST-segment length were significantly associated with osteoporosis.Regression analysis showed that for each additional year of age,the likelihood of developing osteoporosis increased by 1.058 times;females were 2.865 times more likely to develop osteoporosis compared to males;the longer the ECG ST-segment,the higher the likelihood of osteoporosis.Conclusion:Gender,age,and ECG ST-segment length are significantly associated with osteoporosis.These indicators can provide reference points for early identification,early intervention,and reducing the incidence of osteoporosis in clinical settings.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups(Grant Number RGP.2/246/44),B.B.,and https://www.kku.edu.sa/en.
文摘Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.
基金supported by the research project—Application of Machine Learning Methods for Early Diagnosis of Pathologies of the Cardiovascular System funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan.Grant No.IRN AP13068289.
文摘This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart arrhythmias.The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency.The model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals.The integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia classification.Evaluated on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.
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.
文摘Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identifying arrhythmias.The evolving landscape of fabric electrodes,specifically designed for the prolonged monitoring of human ECG signals,is the focus of this research.Adhering to the preferred reporting items for systematic reviews and meta-analyses(PRISMA)statement and assimilating data from 81 pertinent studies sourced from reputable databases,the research conducts a comprehensive systematic review and meta-analysis on the materials,fabric structures and preparation methods of fabric electrodes in the existing literature.It provides a nuanced assessment of the advantages and disadvantages of diverse textile materials and structures,elucidating their impacts on the stability of biomonitoring signals.Furthermore,the study outlines current developmental constraints and future trajectories for fabric electrodes.These insights could serve as essential guidance for ECG monitoring system designers,aiding them in the selection of materials that optimize the measurement of biopotential signals.
文摘Atrial fibrillation (AF) is the most common chronic arrhythmia in clinical practice, which can cause high disability and mortality with the progress of the disease. Many studies at home and abroad have shown that the incidence of atrial fibrillation gradually increases with age. Clinically, the onset of most AF patients is insidious, which is difficult to capture by routine electrocardiogram, and there is some difficulty in the diagnosis. In order to make the early diagnosis of atrial fibrillation more efficient and accurate, this paper reviews the current status and research progress of detection technology for atrial fibrillation at home and abroad, in order to provide a scientific basis for the early diagnosis of atrial fibrillation.
文摘The two most frequent causes of paroxysmal SVT are atrioventricular tachycardia (AVRT) and atrioventricular nodal re-entrant tachycardia (AVNRT). The purpose of this study was to assess the diagnostic efficacy of traditional and newly proposed ECG criteria in the identification of Avnrt and Avrt. Aim of the Study: The aim of this study was to evaluate Atrioventricular Nodal Reentrant Tachycardia (AVNRT) and Atrioventricular Re-entrant Tachycardia (AVRT) using both traditional and novel criteria. Methods: This prospective observational study was conducted at the Electrophysiology Unit, Department of Cardiology, National Institute of Cardiovascular Diseases (NICVD) in Dhaka, from February 2019 to January 2020. A total of 62 patients with Supraventricular Tachycardia (SVT) undergoing electrophysiology study (EPS) were included. Standard ECG criteria were applied for the differential diagnosis, and electrophysiological diagnoses were made using established criteria. Statistical analysis, including descriptive statistics and appropriate tests, was performed using SPSS 23.0. Result: In our study of 62 patients with Supraventricular Tachycardia (SVT), we found that 66.1% had AVNRT and 33.9% had AVRT. The mean age in AVNRT was higher than AVRT (41.3 ± 9.7 vs. 38.5 ± 14.3, p = 0.36) with statistically no significant difference, with similar gender distribution between AVNRT and AVRT groups. Classical AVNRT criteria were present in 30.6% of patients, and 45.2% showed a Pseudo R' wave in aVR. Additionally, 30.6% had an RP interval ≥100ms, more prevalent in AVRT patients (66.7%). Conclusion: Integrating traditional and novel criteria, including lead aVR analysis, enhances the electrocardiographic diagnosis of AVNRT and AVRT, offering a pathway to refined patient care.
文摘Objective:To investigate and thoroughly understand the physical examination results of retired employees from a certain unit in Beijing,analyze their bone mineral density(BMD),and identify risk factors that may indicate osteoporosis.This provides a reference for the individualized prevention,identification,and control of osteoporosis among retired employees.Methods:The bone mineral density and potential factors of 148 retired employees from a unit in 2023 were analyzed and categorized into osteoporosis and non-osteoporosis groups.Key factors from the physical examinations of the two groups were compared.Spearman’s correlation analysis was used to determine the correlation between key factors and osteoporosis.Significant key factors were included in a regression analysis.A multivariate binary logistics regression was employed to identify risk factors indicative of osteoporosis.Results:Correlation analysis revealed that gender,age,and ECG ST-segment length were significantly associated with osteoporosis.Regression analysis showed that for each additional year of age,the likelihood of developing osteoporosis increased by 1.058 times;females were 2.865 times more likely to develop osteoporosis compared to males;the longer the ECG ST-segment,the higher the likelihood of osteoporosis.Conclusion:Gender,age,and ECG ST-segment length are significantly associated with osteoporosis.These indicators can provide reference points for early identification,early intervention,and reducing the incidence of osteoporosis in clinical settings.
