Electrocardiograms (ECG) of Eremias multiocellata were studied at 5-35℃ in body temperature. Electrocardiogram wave intervals (R-R,P-R,QRS,T-P,and R-T) shortened while heart rate increased with the increasing of bod...Electrocardiograms (ECG) of Eremias multiocellata were studied at 5-35℃ in body temperature. Electrocardiogram wave intervals (R-R,P-R,QRS,T-P,and R-T) shortened while heart rate increased with the increasing of body temperature. The average heart rate was 14.6/min at 5℃,whereas it was 201/min at 35℃. The duration of wave intervals of ECG and the heart rate were related significantly to the body temperature (P<0.001). Among the components of a cardiac cycle the cardiac rest period (TP intervals) and the atria-ventricular conduction time (PR interval) were affected mostly by body temperature. In the other hand the ventricular depolarization and repolarization (QRS and R-T intervals) were relatively less affected by the body temperature. The increasing of heart rate with body temperature was mainly caused by the shortening of ECG wave intervals,and the T-P interval (the cardiac rest period) was shortened more noticeably than other intervals.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
BACKGROUND The development of hepatocellular carcinoma(HCC)is influenced by multiple factors.Interventional therapy offers an effective treatment option for patients with unresectable intermediate-to-advanced HCC.Inte...BACKGROUND The development of hepatocellular carcinoma(HCC)is influenced by multiple factors.Interventional therapy offers an effective treatment option for patients with unresectable intermediate-to-advanced HCC.Interventional therapy can induce electrocardiographic(ECG)abnormalities that may be associated with liver dysfunction,electrolyte disorders,and cardiac injury.AIM To explore the ECG alterations and determinants following interventional therapy in patients with HCC.METHODS Sixty patients undergoing interventional treatment for liver cancer were selected as study participants.According to the results of the dynamic ECG examination 1 day after surgery,the patients were divided into an abnormal group(n=21)and a nonabnormal group(n=39).With the help of dynamic ECG examination,the ECG parameters were compared and the baseline data of patients was recorded in the two groups.RESULTS The 24 hours QT interval variability,24 hours normal atrial polarization to ventricular polarization(R-R)interval(standard deviation),24 hours consecutive 5 minutes normal R-R interval,and 24 hours continuous 5 minutes normal R-R interval(standard deviation mean)were lower than patients in the nonabnormal group(P<0.05).The logistic analysis showed that age>60 years,liver function grade B,and postoperative body temperature 38°C were risk factors for abnormal dynamic electrocardiogram in patients with liver cancer intervention(P<0.05).CONCLUSION Interventional therapy for HCC can lead to ECG abnormalities,underscoring the clinical need for enhanced cardiac monitoring to mitigate myocardial complications.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
With the rapid advancement and widespread adoption of new artificial intelligence(AI)technologies,personalized medicine and more accurate diagnosis using medical imaging are now possible.Among its many applications,AI...With the rapid advancement and widespread adoption of new artificial intelligence(AI)technologies,personalized medicine and more accurate diagnosis using medical imaging are now possible.Among its many applications,AI has shown remarkable potential in the analysis of electrocardiograms(ECGs),which provide essential insights into the electrical activity of the heart and allowing early detection of ischemic heart disease(IHD).Notably,single-lead ECG(SLECG)analysis has emerged as a key focus in recent research due to its potential for widespread and efficient screening.This editorial focuses on the latest research progress of AI-enabled SLECG utilized in the diagnosis of IHD.展开更多
Objective:To investigate the diagnostic status and electrocardiographic correlates in patients with biochemical evidence of iron overload.Methods:We conducted a retrospective cohort study of patients in our hospital w...Objective:To investigate the diagnostic status and electrocardiographic correlates in patients with biochemical evidence of iron overload.Methods:We conducted a retrospective cohort study of patients in our hospital with ferritin levels exceeding 500 ng/mL between January 1,2011,and October 24,2022(corresponding to the pre-COVID-19 pandemic period in Beijing).Using ICD-10-CM coded medical records,we assessed the following:definitive diagnostic characterization(genetic or acquired),electrocardiographic(ECG)completion rates,and the prevalence of ECG abnormalities.Statistical analyses,encompassing chi-square tests and correlation studies,were performed using SPSS Statistics software(version 27.0).Results:Except for cases of malignancy,infectious diseases,hematological diseases,chronic diseases,for the unexplained diagnosis group found elevated ferritin during annual health checkup,there were 17 cases in the group with ferritin above 1,000 ng/ml and 36 cases in the group with ferritin ranging from 500 to 1,000 ng/ml,accounting for 23.2%and 25.8%of the entire ferritin analysis respectively,and the total proportion in the entire analysis was 24.0%.Among the cases indicating ferritin higher than 500ng/ml,24.0%of the cases were of unknown diagnosis.ECG acquisition rate for was 55.7%,with 24% demonstrating abnormalities,including atrial fibrillation,sinus tachycardia arrhythmia,atrioventricular block,prolonged QT interval,T-wave inversion,and ST-segment depression.Conclusion:The study revealed that the proportion of unexplained diagnoses of ferritin overload remains relatively high,and the analysis of the ECG is also insufficient.There is a need to enhance clinicians’awareness and attention to iron overload in both diagnosis and ECG analysis.展开更多
BACKGROUND Arm-implanted totally implantable venous access devices(peripherally inserted central catheter port)have become an important vascular access for colorectal cancer chemotherapy,but traditional anatomical lan...BACKGROUND Arm-implanted totally implantable venous access devices(peripherally inserted central catheter port)have become an important vascular access for colorectal cancer chemotherapy,but traditional anatomical landmark positioning techniques have issues with inaccurate positioning and high complication rates.AIM To evaluate the clinical value of image pre-measurement combined with intracavitary electrocardiogram(IC-ECG)positioning technology in arm port implantation for colorectal cancer patients.METHODS A retrospective analysis was conducted on 216 colorectal cancer patients who received arm port implantation in our hospital from January 2024 to December 2024.Patients were divided into an experimental group(image pre-measurement combined with IC-ECG positioning technology,n=103)and a control group(traditional anatomical landmark positioning technique,n=113).Technical success rate,operation time,catheter tip position accuracy,number of intraoperative catheter adjustments,X-ray exposure time,and postoperative complication rates were compared between the two groups.RESULTS The experimental group demonstrated superior outcomes compared to the control group across all key measures.Technical success rate was higher(98.4%vs 92.7%,P<0.05)with significantly reduced operation time(23.6±5.2 minutes vs 31.5±7.8 minutes,P<0.01).Catheter tip positioning accuracy improved substantially(97.6%vs 85.4%,P=0.002)while X-ray exposure time decreased by 71.8%(5.3±2.1 seconds vs 18.7±4.5 seconds,P<0.001).Threemonth complication rates were markedly lower in the experimental group(4.1%vs 14.6%,P=0.008),including significant reductions in catheter-related thrombosis(0.8%vs 4.9%),displacement(1.6%vs 5.7%),and occlusion(1.6%vs 4.1%).Multivariate analysis identified traditional technique as the strongest risk factor(odds ratio=4.27,P<0.001),while the combined IC-ECG approach was protective(odds ratio=0.34 for displacement,P=0.018).Long-term outcomes favored the experimental group with higher chemotherapy completion rates(97.1%vs 88.5%,P=0.014)and longer catheter dwelling time(189.5±45.3 days vs 162.7±53.8 days,P<0.001).CONCLUSION Image pre-measurement combined with intracavitary electrocardiogram positioning technology in arm port implantation for colorectal cancer patients can significantly improve catheter tip positioning accuracy,reduce operation time and X-ray exposure.展开更多
Objective:To investigate the effect of 12-lead electrocardiogram and 24-hour dynamic electrocardiogram in detecting pacemaker dysfunction and changes in cardiac function indexes in patients with pacemaker implantation...Objective:To investigate the effect of 12-lead electrocardiogram and 24-hour dynamic electrocardiogram in detecting pacemaker dysfunction and changes in cardiac function indexes in patients with pacemaker implantation.Methods:A total of 136 patients with pacemaker implantation in the First Clinical Medical College of Three Gorges University,Institute of Cardiovascular Disease of Three Gorges University and Yicang Central People’s Hospital from January 2023 to December 2024 were selected as the research objects.All patients received 12-lead electrocardiogram and 24-hour holter 3–14 days after implantation.Results:The overall detection rate of various types of pacemaker dysfunction by Holter was significantly higher than that by conventional ECG(27.21%vs.5.15%,χ^(2)=24.402,P<0.001).The overall arrhythmia detection rate of Holter was significantly higher than that of conventional electrocardiogram(57.35%vs.10.29%,χ^(2)=67.277,P<0.001).The time domain indexes of heart rate variability obtained by 24-hour continuous monitoring of Holter were significantly improved compared with those of conventional electrocardiogram(P<0.05).Conclusions:Compared with 12-lead electrocardiogram,24-hour holter monitoring can more accurately detect pacemaker dysfunction and arrhythmia in patients with pacemaker implantation,and provide more comprehensive data of heart rate variability,which is helpful for clinicians to better evaluate the cardiac function of patients and adjust treatment plans.展开更多
Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnos...Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.展开更多
Many studies have shown the negative relationship between long term exposure to PM_(2.5)and cardiac dysfunction.Recently,studies have shown that even a single exposure of PM_(2.5)from air sample in permissible range c...Many studies have shown the negative relationship between long term exposure to PM_(2.5)and cardiac dysfunction.Recently,studies have shown that even a single exposure of PM_(2.5)from air sample in permissible range can induce very mild cardiac pathological changes.In the present study,we revisited the toxic effect of PM_(2.5)on rat heart by adopting single and multiple exposure durations.FemaleWistar ratswere exposed to PM_(2.5)at a concentration of 250μg/m3 daily for 3 hr for single(1 day)and multiple(7,14,21 days)durations.The major pathological changes noted in 21 days exposed myocardium comprised of an elevated ST segment(the segment between the S wave and the T wave),development of cardiac fibrosis,hypertrophy,cardiac injury,tissue inflammation and declined cardiac function.With 14 days exposed heart,the electrocardiograms(ECG),data showed insignificantly declined heart rate and an increased QT(the time from the start of the Q wave to the end of the T wave)interval along with mild fibrosis,hypertrophy and lesser number of TUNEL positive cells.On the other hand,single-and 7-days exposure to PM_(2.5)did not impart any significant changes in the myocardium.To determine the reversibility potential of PM_(2.5)induced cardiotoxicity,a washout period of 24 hours was adopted and all observed changes in the myocardium were reversed till day 7,but not in 14-and 21-days exposed samples.Based on the above findings we concluded that PM_(2.5)associated cardiac dysfunction is the cumulative outcome of ineffective cardiac adaptive and repair process that accumulate additively over the time due to prolonged exposure durations.展开更多
BACKGROUND A significant proportion of cancer patients experience autonomic dysfunction,and cancer treatments such as chemotherapy and radiation therapy can exacerbate impairments in the cardiac autonomic nervous syst...BACKGROUND A significant proportion of cancer patients experience autonomic dysfunction,and cancer treatments such as chemotherapy and radiation therapy can exacerbate impairments in the cardiac autonomic nervous system.This study sought to investigate the characteristics of heart rate variability(HRV)in individuals with cancer.AIM To evaluate the relationship between HRV and cancer patients,providing insights and references for cancer treatment.METHODS The study included 127 cancer patients with available 24-hour dynamic electrocardiogram data.HRV differences were analyzed using both time domain and frequency domain methods.These findings were then compared to HRV data from reference individuals,sourced from literature that utilized the same HRV computing algorithm.RESULTS Our findings revealed that cancer patients generally exhibited abnormal HRV compared to the reference group.HRV was found to be correlated with age and clinical type(P<0.05),but no significant correlation was observed with tumor site or gender(P>0.05).CONCLUSION This study indicates that cancer patients have significantly abnormal HRV compared to reference individuals,suggesting the presence of a certain level of cardiac autonomic dysfunction in this patient population.展开更多
Objective:To explore the correlation between night ECG parameters and sleep quality in elderly patients with atrial premature beat(PAC).Methods:A total of 307 elderly patients with PAC were selected from March 2022 to...Objective:To explore the correlation between night ECG parameters and sleep quality in elderly patients with atrial premature beat(PAC).Methods:A total of 307 elderly patients with PAC were selected from March 2022 to March 2024.The parameters of room morning load and heart rate variability(HRV)at night(22:00-6:00)were collected by 24h holter electrocardiogram,and the sleep quality of PAC patients was evaluated by Pittsburgh Sleep Quality Index(PSQI).Multiple regression analysis was used to explore the correlation between night ECG parameters and sleep quality.Results:The incidence of sleep disorder in 307 elderly PAC patients was 62.54%.Univariate analysis showed that there were no statistically significant differences in gender,BMI and education level(P>0.05),but there were statistically significant differences in age,disease course,underlying diseases,atrial morning load,SDNN,RMSSD,LF,HF,LF/HF and TP(P<0.05).Multivariate Logistic regression analysis showed that atrial morning load,SDNN,RMSSD,LF and LF/HF were independent influencing factors of sleep disorder in elderly PAC patients(P<0.05).Patients with sleep disorders were divided into mild group,moderate group and severe group according to PSQI score,and there were statistically significant differences in the indexes of atrial morning load,SDNN,LF,LF/HF and TP among the three groups(all P<0.05).Spearman correlation analysis showed that room morning load,LF,LF/HF and TP were positively correlated with the degree of sleep disorder,while SDNN parameters were negatively correlated with the degree of sleep disorder(all P<0.05).Conclusion:There is a significant correlation between night ECG parameters and sleep disorders in elderly patients with PAC.It is possible to prevent and treat sleep disorders by monitoring ECG abnormalities and improve the reliability of treatment.展开更多
Wellens’ syndrome is defined by specific T-wave inversions in the precordial leads of the electrocardiogram (ECG),which are indicative of acute anterior myocardial ischemia caused by severe proximal stenosis of the l...Wellens’ syndrome is defined by specific T-wave inversions in the precordial leads of the electrocardiogram (ECG),which are indicative of acute anterior myocardial ischemia caused by severe proximal stenosis of the left anterior descending (LAD)artery.If not promptly treated,approximately 75%of patients with Wellens’ syndrome may experience extensive anterior wall myocardial infarction or sudden cardiac death within days to weeks.^([1,2]) Although the characteristic ECG changes associated with Wellens’syndrome are highly suggestive of LAD occlusion,there are rare instances in which similar ECG alterations are observed in the absence of LAD stenosis,a phenomenon referred to as pseudo-Wellens’ syndrome.The precise pathophysiological mechanisms underlying this syndrome remain unclear.Here,we present a patient with a myocardial bridge who presented a typical Wellens’ECG pattern.展开更多
Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting ca...Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age(ECG Age)using sophisticated signal processing and deep learning techniques.This study looks at six main heart conditions found in 12-lead electrocardiogram(ECG)data.It addresses important issues like class imbalances,missing lead scenarios,and model generalizations.A modified residual neural network(ResNet)architecture was developed to enhance the detection of cardiac abnormalities.Results:The proposed ResNet demonst rated superior performance when compared with two linear models and an alternative ResNet architectures,achieving an overall classification accuracy of 91.25%and an F1 score of 93.9%,surpassing baseline models.A comprehensive lead loss analysis was conducted,evaluating model performance across 4096 combinations of missing leads.The results revealed that pulse rate-based factors remained robust with up to 75%lead loss,while block-based factors experienced significant performance declines beyond the loss of four leads.Conclusion:This analysis highlighted the importance of addressing lead loss impacts to maintain a robust model.To optimize performance,targeted training approaches were developed for different conditions.Based on these insights,a grouping strategy was implemented to train specialized models for pulse rate-based and block-based conditions.This approach resulted in notable improvements,achieving an overall classification accuracy of 95.12%and an F1 score of 95.79%.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met...Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.展开更多
Sparse decomposition is a new theory in signal processing,with the advantage in that the base(dictionary)used in this theory is over-complete,and can reflect the nature of a signal.Thus,the sparse decomposition of sig...Sparse decomposition is a new theory in signal processing,with the advantage in that the base(dictionary)used in this theory is over-complete,and can reflect the nature of a signal.Thus,the sparse decomposition of signal can obtain sparse representation,which is very important in data compression.The algorithm of compression based on sparse decomposition is investigated.By training on and learning electrocardiogram(ECG)data in the MIT-BIH Arrhythmia Database,we constructed an over-complete dictionary of ECGs.Since the atoms in this dictionary are in accord with the character of ECGs,it is possible that an extensive ECG datum is reconstructed by a few nonzero coefficients and atoms.The proposed compression algorithm can adjust compression ratio according to practical request,and the distortion is low(when the compression ratio is 20∶1,the standard error is 5.11%).The experiments prove the feasibility of the proposed compression algorithm.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
文摘Electrocardiograms (ECG) of Eremias multiocellata were studied at 5-35℃ in body temperature. Electrocardiogram wave intervals (R-R,P-R,QRS,T-P,and R-T) shortened while heart rate increased with the increasing of body temperature. The average heart rate was 14.6/min at 5℃,whereas it was 201/min at 35℃. The duration of wave intervals of ECG and the heart rate were related significantly to the body temperature (P<0.001). Among the components of a cardiac cycle the cardiac rest period (TP intervals) and the atria-ventricular conduction time (PR interval) were affected mostly by body temperature. In the other hand the ventricular depolarization and repolarization (QRS and R-T intervals) were relatively less affected by the body temperature. The increasing of heart rate with body temperature was mainly caused by the shortening of ECG wave intervals,and the T-P interval (the cardiac rest period) was shortened more noticeably than other intervals.
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BACKGROUND The development of hepatocellular carcinoma(HCC)is influenced by multiple factors.Interventional therapy offers an effective treatment option for patients with unresectable intermediate-to-advanced HCC.Interventional therapy can induce electrocardiographic(ECG)abnormalities that may be associated with liver dysfunction,electrolyte disorders,and cardiac injury.AIM To explore the ECG alterations and determinants following interventional therapy in patients with HCC.METHODS Sixty patients undergoing interventional treatment for liver cancer were selected as study participants.According to the results of the dynamic ECG examination 1 day after surgery,the patients were divided into an abnormal group(n=21)and a nonabnormal group(n=39).With the help of dynamic ECG examination,the ECG parameters were compared and the baseline data of patients was recorded in the two groups.RESULTS The 24 hours QT interval variability,24 hours normal atrial polarization to ventricular polarization(R-R)interval(standard deviation),24 hours consecutive 5 minutes normal R-R interval,and 24 hours continuous 5 minutes normal R-R interval(standard deviation mean)were lower than patients in the nonabnormal group(P<0.05).The logistic analysis showed that age>60 years,liver function grade B,and postoperative body temperature 38°C were risk factors for abnormal dynamic electrocardiogram in patients with liver cancer intervention(P<0.05).CONCLUSION Interventional therapy for HCC can lead to ECG abnormalities,underscoring the clinical need for enhanced cardiac monitoring to mitigate myocardial complications.
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.
基金Supported by National Natural Science Foundation of China,No.82170327 and No.82370332Tianjin Key Medical Discipline(Specialty)Construction Project,No.TJYXZDXK-029A.
文摘With the rapid advancement and widespread adoption of new artificial intelligence(AI)technologies,personalized medicine and more accurate diagnosis using medical imaging are now possible.Among its many applications,AI has shown remarkable potential in the analysis of electrocardiograms(ECGs),which provide essential insights into the electrical activity of the heart and allowing early detection of ischemic heart disease(IHD).Notably,single-lead ECG(SLECG)analysis has emerged as a key focus in recent research due to its potential for widespread and efficient screening.This editorial focuses on the latest research progress of AI-enabled SLECG utilized in the diagnosis of IHD.
文摘Objective:To investigate the diagnostic status and electrocardiographic correlates in patients with biochemical evidence of iron overload.Methods:We conducted a retrospective cohort study of patients in our hospital with ferritin levels exceeding 500 ng/mL between January 1,2011,and October 24,2022(corresponding to the pre-COVID-19 pandemic period in Beijing).Using ICD-10-CM coded medical records,we assessed the following:definitive diagnostic characterization(genetic or acquired),electrocardiographic(ECG)completion rates,and the prevalence of ECG abnormalities.Statistical analyses,encompassing chi-square tests and correlation studies,were performed using SPSS Statistics software(version 27.0).Results:Except for cases of malignancy,infectious diseases,hematological diseases,chronic diseases,for the unexplained diagnosis group found elevated ferritin during annual health checkup,there were 17 cases in the group with ferritin above 1,000 ng/ml and 36 cases in the group with ferritin ranging from 500 to 1,000 ng/ml,accounting for 23.2%and 25.8%of the entire ferritin analysis respectively,and the total proportion in the entire analysis was 24.0%.Among the cases indicating ferritin higher than 500ng/ml,24.0%of the cases were of unknown diagnosis.ECG acquisition rate for was 55.7%,with 24% demonstrating abnormalities,including atrial fibrillation,sinus tachycardia arrhythmia,atrioventricular block,prolonged QT interval,T-wave inversion,and ST-segment depression.Conclusion:The study revealed that the proportion of unexplained diagnoses of ferritin overload remains relatively high,and the analysis of the ECG is also insufficient.There is a need to enhance clinicians’awareness and attention to iron overload in both diagnosis and ECG analysis.
基金Supported by the Affiliated Hospital of Xuzhou Medical University,No.2024ZH04Xuzhou Municipal Science and Technology Bureau,No.KC23282.
文摘BACKGROUND Arm-implanted totally implantable venous access devices(peripherally inserted central catheter port)have become an important vascular access for colorectal cancer chemotherapy,but traditional anatomical landmark positioning techniques have issues with inaccurate positioning and high complication rates.AIM To evaluate the clinical value of image pre-measurement combined with intracavitary electrocardiogram(IC-ECG)positioning technology in arm port implantation for colorectal cancer patients.METHODS A retrospective analysis was conducted on 216 colorectal cancer patients who received arm port implantation in our hospital from January 2024 to December 2024.Patients were divided into an experimental group(image pre-measurement combined with IC-ECG positioning technology,n=103)and a control group(traditional anatomical landmark positioning technique,n=113).Technical success rate,operation time,catheter tip position accuracy,number of intraoperative catheter adjustments,X-ray exposure time,and postoperative complication rates were compared between the two groups.RESULTS The experimental group demonstrated superior outcomes compared to the control group across all key measures.Technical success rate was higher(98.4%vs 92.7%,P<0.05)with significantly reduced operation time(23.6±5.2 minutes vs 31.5±7.8 minutes,P<0.01).Catheter tip positioning accuracy improved substantially(97.6%vs 85.4%,P=0.002)while X-ray exposure time decreased by 71.8%(5.3±2.1 seconds vs 18.7±4.5 seconds,P<0.001).Threemonth complication rates were markedly lower in the experimental group(4.1%vs 14.6%,P=0.008),including significant reductions in catheter-related thrombosis(0.8%vs 4.9%),displacement(1.6%vs 5.7%),and occlusion(1.6%vs 4.1%).Multivariate analysis identified traditional technique as the strongest risk factor(odds ratio=4.27,P<0.001),while the combined IC-ECG approach was protective(odds ratio=0.34 for displacement,P=0.018).Long-term outcomes favored the experimental group with higher chemotherapy completion rates(97.1%vs 88.5%,P=0.014)and longer catheter dwelling time(189.5±45.3 days vs 162.7±53.8 days,P<0.001).CONCLUSION Image pre-measurement combined with intracavitary electrocardiogram positioning technology in arm port implantation for colorectal cancer patients can significantly improve catheter tip positioning accuracy,reduce operation time and X-ray exposure.
文摘Objective:To investigate the effect of 12-lead electrocardiogram and 24-hour dynamic electrocardiogram in detecting pacemaker dysfunction and changes in cardiac function indexes in patients with pacemaker implantation.Methods:A total of 136 patients with pacemaker implantation in the First Clinical Medical College of Three Gorges University,Institute of Cardiovascular Disease of Three Gorges University and Yicang Central People’s Hospital from January 2023 to December 2024 were selected as the research objects.All patients received 12-lead electrocardiogram and 24-hour holter 3–14 days after implantation.Results:The overall detection rate of various types of pacemaker dysfunction by Holter was significantly higher than that by conventional ECG(27.21%vs.5.15%,χ^(2)=24.402,P<0.001).The overall arrhythmia detection rate of Holter was significantly higher than that of conventional electrocardiogram(57.35%vs.10.29%,χ^(2)=67.277,P<0.001).The time domain indexes of heart rate variability obtained by 24-hour continuous monitoring of Holter were significantly improved compared with those of conventional electrocardiogram(P<0.05).Conclusions:Compared with 12-lead electrocardiogram,24-hour holter monitoring can more accurately detect pacemaker dysfunction and arrhythmia in patients with pacemaker implantation,and provide more comprehensive data of heart rate variability,which is helpful for clinicians to better evaluate the cardiac function of patients and adjust treatment plans.
基金funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant numbers AP14969403 and AP23485820.
文摘Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.
基金the Indian Council of Medical Research(No.3/1/2(22)/Env/2021-NCD-II)Department of science and technology,India(CRG/2021/000227)for funding this research.
文摘Many studies have shown the negative relationship between long term exposure to PM_(2.5)and cardiac dysfunction.Recently,studies have shown that even a single exposure of PM_(2.5)from air sample in permissible range can induce very mild cardiac pathological changes.In the present study,we revisited the toxic effect of PM_(2.5)on rat heart by adopting single and multiple exposure durations.FemaleWistar ratswere exposed to PM_(2.5)at a concentration of 250μg/m3 daily for 3 hr for single(1 day)and multiple(7,14,21 days)durations.The major pathological changes noted in 21 days exposed myocardium comprised of an elevated ST segment(the segment between the S wave and the T wave),development of cardiac fibrosis,hypertrophy,cardiac injury,tissue inflammation and declined cardiac function.With 14 days exposed heart,the electrocardiograms(ECG),data showed insignificantly declined heart rate and an increased QT(the time from the start of the Q wave to the end of the T wave)interval along with mild fibrosis,hypertrophy and lesser number of TUNEL positive cells.On the other hand,single-and 7-days exposure to PM_(2.5)did not impart any significant changes in the myocardium.To determine the reversibility potential of PM_(2.5)induced cardiotoxicity,a washout period of 24 hours was adopted and all observed changes in the myocardium were reversed till day 7,but not in 14-and 21-days exposed samples.Based on the above findings we concluded that PM_(2.5)associated cardiac dysfunction is the cumulative outcome of ineffective cardiac adaptive and repair process that accumulate additively over the time due to prolonged exposure durations.
基金the Medical Ethics Committee of the Hefei Cancer Hospital,Chinese Academy of Sciences(No.PJ-KY-2024-025).
文摘BACKGROUND A significant proportion of cancer patients experience autonomic dysfunction,and cancer treatments such as chemotherapy and radiation therapy can exacerbate impairments in the cardiac autonomic nervous system.This study sought to investigate the characteristics of heart rate variability(HRV)in individuals with cancer.AIM To evaluate the relationship between HRV and cancer patients,providing insights and references for cancer treatment.METHODS The study included 127 cancer patients with available 24-hour dynamic electrocardiogram data.HRV differences were analyzed using both time domain and frequency domain methods.These findings were then compared to HRV data from reference individuals,sourced from literature that utilized the same HRV computing algorithm.RESULTS Our findings revealed that cancer patients generally exhibited abnormal HRV compared to the reference group.HRV was found to be correlated with age and clinical type(P<0.05),but no significant correlation was observed with tumor site or gender(P>0.05).CONCLUSION This study indicates that cancer patients have significantly abnormal HRV compared to reference individuals,suggesting the presence of a certain level of cardiac autonomic dysfunction in this patient population.
文摘Objective:To explore the correlation between night ECG parameters and sleep quality in elderly patients with atrial premature beat(PAC).Methods:A total of 307 elderly patients with PAC were selected from March 2022 to March 2024.The parameters of room morning load and heart rate variability(HRV)at night(22:00-6:00)were collected by 24h holter electrocardiogram,and the sleep quality of PAC patients was evaluated by Pittsburgh Sleep Quality Index(PSQI).Multiple regression analysis was used to explore the correlation between night ECG parameters and sleep quality.Results:The incidence of sleep disorder in 307 elderly PAC patients was 62.54%.Univariate analysis showed that there were no statistically significant differences in gender,BMI and education level(P>0.05),but there were statistically significant differences in age,disease course,underlying diseases,atrial morning load,SDNN,RMSSD,LF,HF,LF/HF and TP(P<0.05).Multivariate Logistic regression analysis showed that atrial morning load,SDNN,RMSSD,LF and LF/HF were independent influencing factors of sleep disorder in elderly PAC patients(P<0.05).Patients with sleep disorders were divided into mild group,moderate group and severe group according to PSQI score,and there were statistically significant differences in the indexes of atrial morning load,SDNN,LF,LF/HF and TP among the three groups(all P<0.05).Spearman correlation analysis showed that room morning load,LF,LF/HF and TP were positively correlated with the degree of sleep disorder,while SDNN parameters were negatively correlated with the degree of sleep disorder(all P<0.05).Conclusion:There is a significant correlation between night ECG parameters and sleep disorders in elderly patients with PAC.It is possible to prevent and treat sleep disorders by monitoring ECG abnormalities and improve the reliability of treatment.
基金supported by a grant from the Science and Technology Fund of Tianjin Municipal Education Commission(2021KJ216)。
文摘Wellens’ syndrome is defined by specific T-wave inversions in the precordial leads of the electrocardiogram (ECG),which are indicative of acute anterior myocardial ischemia caused by severe proximal stenosis of the left anterior descending (LAD)artery.If not promptly treated,approximately 75%of patients with Wellens’ syndrome may experience extensive anterior wall myocardial infarction or sudden cardiac death within days to weeks.^([1,2]) Although the characteristic ECG changes associated with Wellens’syndrome are highly suggestive of LAD occlusion,there are rare instances in which similar ECG alterations are observed in the absence of LAD stenosis,a phenomenon referred to as pseudo-Wellens’ syndrome.The precise pathophysiological mechanisms underlying this syndrome remain unclear.Here,we present a patient with a myocardial bridge who presented a typical Wellens’ECG pattern.
文摘Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age(ECG Age)using sophisticated signal processing and deep learning techniques.This study looks at six main heart conditions found in 12-lead electrocardiogram(ECG)data.It addresses important issues like class imbalances,missing lead scenarios,and model generalizations.A modified residual neural network(ResNet)architecture was developed to enhance the detection of cardiac abnormalities.Results:The proposed ResNet demonst rated superior performance when compared with two linear models and an alternative ResNet architectures,achieving an overall classification accuracy of 91.25%and an F1 score of 93.9%,surpassing baseline models.A comprehensive lead loss analysis was conducted,evaluating model performance across 4096 combinations of missing leads.The results revealed that pulse rate-based factors remained robust with up to 75%lead loss,while block-based factors experienced significant performance declines beyond the loss of four leads.Conclusion:This analysis highlighted the importance of addressing lead loss impacts to maintain a robust model.To optimize performance,targeted training approaches were developed for different conditions.Based on these insights,a grouping strategy was implemented to train specialized models for pulse rate-based and block-based conditions.This approach resulted in notable improvements,achieving an overall classification accuracy of 95.12%and an F1 score of 95.79%.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
文摘Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.
文摘Sparse decomposition is a new theory in signal processing,with the advantage in that the base(dictionary)used in this theory is over-complete,and can reflect the nature of a signal.Thus,the sparse decomposition of signal can obtain sparse representation,which is very important in data compression.The algorithm of compression based on sparse decomposition is investigated.By training on and learning electrocardiogram(ECG)data in the MIT-BIH Arrhythmia Database,we constructed an over-complete dictionary of ECGs.Since the atoms in this dictionary are in accord with the character of ECGs,it is possible that an extensive ECG datum is reconstructed by a few nonzero coefficients and atoms.The proposed compression algorithm can adjust compression ratio according to practical request,and the distortion is low(when the compression ratio is 20∶1,the standard error is 5.11%).The experiments prove the feasibility of the proposed compression algorithm.
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.
