针对注水井分层注水量诊断技术难题,提出基于分布式光纤温度传感(Distributed Temperature Sensing,DTS)的注水井吸水剖面解释方法。建立考虑微量热效应的注水井温度剖面预测模型,模拟分析注水量、注水时间、储层导热系数等7个因素对温...针对注水井分层注水量诊断技术难题,提出基于分布式光纤温度传感(Distributed Temperature Sensing,DTS)的注水井吸水剖面解释方法。建立考虑微量热效应的注水井温度剖面预测模型,模拟分析注水量、注水时间、储层导热系数等7个因素对温度剖面的影响规律。通过正交试验模拟分析,确定不同因素对注水井温度剖面的影响程度从强到弱分别为注入水温度、注水时间、注水量、井筒半径、储层导热系数、井筒倾斜角度、注水层渗透率,明确影响注水井温度剖面的主控因素为注入水温度、注水时间和注入量。采用模拟退火(Simulated Annealing,SA)算法建立注水井DTS数据反演模型,对一口注水井现场实测DTS数据进行反演,获得较为准确的吸水剖面,单层最大吸水量误差百分比14.25%,平均误差11.09%,验证该反演方法的可靠性。通过DTS数据反演可以实现注水井吸水剖面定量解释,为注水效果评价提供直接依据。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
In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and...In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation efficiency.This glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias.Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model selection.To validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our methodology.In the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for training.The second pipeline is dedicated to feature extraction and classification,utilizing deep learning models.Notably,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class arrhythmias.An ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model pipeline.In our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.展开更多
BACKGROUND Individuals diagnosed with gastrointestinal tumors are at an increased risk of developing cardiovascular diseases.Among which,ventricular arrhythmia is a prevalent clinical concern.This suggests that ventri...BACKGROUND Individuals diagnosed with gastrointestinal tumors are at an increased risk of developing cardiovascular diseases.Among which,ventricular arrhythmia is a prevalent clinical concern.This suggests that ventricular arrhythmias may have predictive value in the prognosis of patients with gastrointestinal tumors.AIM To explore the prognostic value of ventricular arrhythmias in patients with gastrointestinal tumors receiving surgery.METHODS We retrospectively analyzed data from 130 patients undergoing gastrointestinal tumor resection.These patients were evaluated by a 24-h ambulatory electrocardiogram(ECG)at the Sixth Affiliated Hospital of Sun Yat-sen University from January 2018 to June 2020.Additionally,41 general healthy age-matched and sexmatched controls were included.Patients were categorized into survival and non-survival groups.The primary endpoint was all-cause mortality,and secondary endpoints included major adverse cardiovascular events(MACEs).RESULTS Colorectal tumors comprised 90%of cases.Preoperative ambulatory ECG monitoring revealed that among the 130 patients with gastrointestinal tumors,100(76.92%)exhibited varying degrees of premature ventricular contractions(PVCs).Ten patients(7.69%)manifested non-sustained ventricular tachycardia(NSVT).The patients with gastrointestinal tumors exhibited higher PVCs compared to the healthy controls on both conventional ECG[27(21.3)vs 1(2.5),P=0.012]and 24-h ambulatory ECG[14(1.0,405)vs 1(0,6.5),P<0.001].Non-survivors had a higher PVC count than survivors[150.50(7.25,1690.50)vs 9(0,229.25),P=0.020].During the follow-up period,24 patients died and 11 patients experienced MACEs.Univariate analysis linked PVC>35/24 h to all-cause mortality,and NSVT was associated with MACE.However,neither PVC burden nor NSVT independently predicted outcomes according to multivariate analysis.CONCLUSION Patients with gastrointestinal tumors exhibited elevated PVCs.PVCs>35/24 h and NSVT detected by 24-h ambulatory ECG were prognostically significant but were not found to be independent predictors.展开更多
This editorial,comments on the article by Spartalis et al published in the recent issue of the World Journal of Cardiology.We here provide an outlook on potential ethical concerns related to the future application of ...This editorial,comments on the article by Spartalis et al published in the recent issue of the World Journal of Cardiology.We here provide an outlook on potential ethical concerns related to the future application of gene therapy in the field of inherited arrhythmias.As monogenic diseases with no or few therapeutic options available through standard care,inherited arrhythmias are ideal candidates to gene therapy in their treatment.Patients with inherited arrhythmias typically have a poor quality of life,especially young people engaged in agonistic sports.While genome editing for treatment of inherited arrhythmias still has theoretical application,advances in CRISPR/Cas9 technology now allows the generation of knock-in animal models of the disease.However,clinical translation is somehow expected soon and this make consistent discussing about ethical concerns related to gene editing in inherited arrhythmias.Genomic off-target activity is a known technical issue,but its relationship with ethnical and individual genetical diversity raises concerns about an equitable accessibility.Meanwhile,the costeffectiveness may further limit an equal distribution of gene therapies.The economic burden of gene therapies on healthcare systems is is increasingly recognized as a pressing concern.A growing body of studies are reporting uncertainty in payback periods with intuitive short-term effects for insurance-based healthcare systems,but potential concerns for universal healthcare systems in the long term as well.Altogether,those aspects strongly indicate a need of regulatory entities to manage those issues.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
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
文摘针对Oligo(d T)亲和层析介质的吸附性能,以poly(A)为模型分子,考察了4种Oligo(d T)亲和层析介质的静态吸附平衡、吸附动力学和动态结合载量(DBC),探讨了载量影响相关机制。结果表明,4种介质的合适吸附条件均为0.6 mol·L-1Na Cl、p H=6~7;Monomix d T20静态吸附容量最大,且poly(A)能扩散至介质微球深层孔内,而Poros Oligo(d T)25、Praesto Jetted (d T)25和Nano Gel d T20等3种介质中poly(A)均主要为表层吸附、静态吸附容量稍低;对于DBC,Nano Gel d T20和Monomix d T20的10%穿透的DBC较高,而Poros Oligo (d T)25和Praesto Jetted (d T)25相对略低。经分析,影响载量的主要因素包含基质种类、微球孔径、配基密度、间隔臂和配基长度等。对于基质种类,聚苯乙烯基质可能孔道结构较为特别。对于微球孔径,应针对不同大小的m RNA分子定制不同孔径的微球,以平衡传质阻力与可及吸附表面积之间的矛盾,从而增大DBC。
文摘针对注水井分层注水量诊断技术难题,提出基于分布式光纤温度传感(Distributed Temperature Sensing,DTS)的注水井吸水剖面解释方法。建立考虑微量热效应的注水井温度剖面预测模型,模拟分析注水量、注水时间、储层导热系数等7个因素对温度剖面的影响规律。通过正交试验模拟分析,确定不同因素对注水井温度剖面的影响程度从强到弱分别为注入水温度、注水时间、注水量、井筒半径、储层导热系数、井筒倾斜角度、注水层渗透率,明确影响注水井温度剖面的主控因素为注入水温度、注水时间和注入量。采用模拟退火(Simulated Annealing,SA)算法建立注水井DTS数据反演模型,对一口注水井现场实测DTS数据进行反演,获得较为准确的吸水剖面,单层最大吸水量误差百分比14.25%,平均误差11.09%,验证该反演方法的可靠性。通过DTS数据反演可以实现注水井吸水剖面定量解释,为注水效果评价提供直接依据。
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In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation efficiency.This glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias.Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model selection.To validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our methodology.In the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for training.The second pipeline is dedicated to feature extraction and classification,utilizing deep learning models.Notably,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class arrhythmias.An ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model pipeline.In our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.
基金Supported by the Sixth Affiliated Hospital of Sun Yat-sen University Clinical Research-1010 Program,No.1010PY(2023)-06the National Nature Science Foundation of China,No.81400301+1 种基金the Fundamental Research Funds for the Central Universities,No.19ykpy10Guangzhou Health Science and Technology Project,No.20231A010068.
文摘BACKGROUND Individuals diagnosed with gastrointestinal tumors are at an increased risk of developing cardiovascular diseases.Among which,ventricular arrhythmia is a prevalent clinical concern.This suggests that ventricular arrhythmias may have predictive value in the prognosis of patients with gastrointestinal tumors.AIM To explore the prognostic value of ventricular arrhythmias in patients with gastrointestinal tumors receiving surgery.METHODS We retrospectively analyzed data from 130 patients undergoing gastrointestinal tumor resection.These patients were evaluated by a 24-h ambulatory electrocardiogram(ECG)at the Sixth Affiliated Hospital of Sun Yat-sen University from January 2018 to June 2020.Additionally,41 general healthy age-matched and sexmatched controls were included.Patients were categorized into survival and non-survival groups.The primary endpoint was all-cause mortality,and secondary endpoints included major adverse cardiovascular events(MACEs).RESULTS Colorectal tumors comprised 90%of cases.Preoperative ambulatory ECG monitoring revealed that among the 130 patients with gastrointestinal tumors,100(76.92%)exhibited varying degrees of premature ventricular contractions(PVCs).Ten patients(7.69%)manifested non-sustained ventricular tachycardia(NSVT).The patients with gastrointestinal tumors exhibited higher PVCs compared to the healthy controls on both conventional ECG[27(21.3)vs 1(2.5),P=0.012]and 24-h ambulatory ECG[14(1.0,405)vs 1(0,6.5),P<0.001].Non-survivors had a higher PVC count than survivors[150.50(7.25,1690.50)vs 9(0,229.25),P=0.020].During the follow-up period,24 patients died and 11 patients experienced MACEs.Univariate analysis linked PVC>35/24 h to all-cause mortality,and NSVT was associated with MACE.However,neither PVC burden nor NSVT independently predicted outcomes according to multivariate analysis.CONCLUSION Patients with gastrointestinal tumors exhibited elevated PVCs.PVCs>35/24 h and NSVT detected by 24-h ambulatory ECG were prognostically significant but were not found to be independent predictors.
文摘This editorial,comments on the article by Spartalis et al published in the recent issue of the World Journal of Cardiology.We here provide an outlook on potential ethical concerns related to the future application of gene therapy in the field of inherited arrhythmias.As monogenic diseases with no or few therapeutic options available through standard care,inherited arrhythmias are ideal candidates to gene therapy in their treatment.Patients with inherited arrhythmias typically have a poor quality of life,especially young people engaged in agonistic sports.While genome editing for treatment of inherited arrhythmias still has theoretical application,advances in CRISPR/Cas9 technology now allows the generation of knock-in animal models of the disease.However,clinical translation is somehow expected soon and this make consistent discussing about ethical concerns related to gene editing in inherited arrhythmias.Genomic off-target activity is a known technical issue,but its relationship with ethnical and individual genetical diversity raises concerns about an equitable accessibility.Meanwhile,the costeffectiveness may further limit an equal distribution of gene therapies.The economic burden of gene therapies on healthcare systems is is increasingly recognized as a pressing concern.A growing body of studies are reporting uncertainty in payback periods with intuitive short-term effects for insurance-based healthcare systems,but potential concerns for universal healthcare systems in the long term as well.Altogether,those aspects strongly indicate a need of regulatory entities to manage those issues.
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.
