Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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.

AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

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.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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.

Prognostic relevance of ventricular arrhythmias in surgical patients with gastrointestinal tumors

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.

Efficient ECG classification based on Chi-square distance for arrhythmia detection

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.

Facing ethical concerns in the age of precise gene therapy:Outlook on inherited arrhythmias

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.

Analysis of the Efficacy of Low-Dose Betaloc Combined with Amiodarone in Treating Ventricular Arrhythmia

Objective:To explore and analyze the clinical effect of low-dose Betaloc combined with amiodarone in treating ventricular arrhythmia.Methods:70 patients with ventricular arrhythmia who were admitted to the Department of Cardiology of our hospital between August 2022 and August 2023 were selected as research subjects.They were divided into two groups using the coin-tossing method:the combination group(n=35)and the reference group(n=35).The combination group was treated with low-dose Betaloc and amiodarone,and the control group was treated with low-dose Betaloc alone.The treatment efficacy,cardiac function indicators,and related tested indicators of the two groups were compared.Results:The total efficacy of the treatment received by the combination group was much higher than that of the control group(P<0.05).Besides,after treatment,the cardiac function indicators such as left ventricular ejection fraction(LVEF),left ventricular end-systolic volume(LVESV),and cardiac index(CI)of the patients in the combination group were significantly better than those of the reference group(P<0.05).Furthermore,the high-sensitivity C-reactive protein(Hs-CRP),N-terminal prohormone of brain natriuretic peptide(NT-proBNP),adiponectin(APN),and other related test indicators of the patients in the combination group were significantly better than those of the reference group(P<0.05).Conclusion:Low-dose Betaloc combined with amiodarone has a noticeable effect in treating ventricular arrhythmia and deserves to be widely promoted.

Clinical Efficacy of Metoprolol Succinate Extended-Release Tablets in the Treatment of Post-Myocardial Infarction Ventricular Arrhythmias

Objective:To investigate the clinical efficacy of metoprolol succinate extended-release tablets in the treatment of post-myocardial infarction ventricular arrhythmias.Methods:The clinical data of 84 patients with post-myocardial infarction ventricular arrhythmia included in the study were collected and they were divided into Groups A and B with 42 cases each using the randomization method.Group A was treated with oral glucosamine hydrochloride,while Group B was administered oral metoprolol succinate extended-release tablets.Combined indicators were used to evaluate the improvement of clinical indicators,therapeutic effects,and the incidence of adverse reactions in the two groups.Results:The baseline data of the two groups of patients were not statistically significant(Pall>0.05);after treatment,the QT dispersion,corrected QT dispersion,and heart rate of Group B were lower than that of Group A(Pall=0.000<0.001);the 2 total clinical effectiveness of Group B was 95.24%,which was significantly higher than 80.95%in Group A(χ=4.087,P=0.043<0.05);the total incidence of adverse reactions in Group B was 4.76%,which was significantly lower than 219.04%in Group A(χ=4.087,P=0.043<0.05).Conclusion:In the treatment of post-myocardial infarction ventricular arrhythmia,the use of metoprolol succinate extended-release tablets can effectively correct the QT dispersion of patients,improve their heart rate,increase clinical effectiveness,and reduce the incidence of adverse reactions.

Electrophysiologic Effects of Sophoridine on a Canine Model of Ischemic Ventricular Tachyarrhythmias

A canine model of ischemic ventricular tachyarrhythmias was established in open-chest dogs subjected to programmed electrical stimulation (PES) for 5￣8 days after acute myocardial infarction. The electrophysiologic effects of sophoridine (Sop) and procainamide (PA) were observed in this canine model. With routine methods of PES, ventricular tachycardia (VT) and ventricular fibrilation (VF) could be reproducibly initiated in this model. Both drugs distinctly lengthened the QTc interval ( P <0.01) and the effective refractory period (ERP) in normal and ischemic ventricular myocardium ( P <0.01), decreased the dispersion of ERP in ischemic myocardium and the dispersion of ERP in left ventricle (P <0.05), and increased the diastolic excitability threshold of normal and ischemic ventricular myocardium remarkably ( P <0.01). Both drugs effectively prevented the PES-induced VT or VF and ischemia-induced VF ( P <0.05). The results indicated that this canine model is a good and reliable one, sophoridine and procainamide may be effective in preventing the onset of reentrant ventricular tachyarrhythmias after myocardial ischemic damage.

以MIT-BIH Arrhythmia Database为标准数据源的心率不齐的ECG模拟信号发生器

利用单片机控制DAC输出的方法,将MIT-BIH Arrhythmia Database中的标准化心率失常数据通过可控增益的DAC输出,供医疗仪器测试使用。数据以FAT格式存储在SM卡中,方便数据库的更换。

Effect and mechanism of Irbesartan on occurrence of ventricular arrhythmias in rats with myocardial ischemia through connexin43(cx43)

Objective: To explore the effect and mechanism of angiotensin II receptor blockers-Irbesartan on occurrence of ventricular arrhythmias in rats with myocardial ischemia. Methods: Rats with embryonic cardiomyocytes-H9c2 were randomly divided into control group. ischemia group. Irbesartan group and Irbesartan+ischemia group. The cell viability of rats in each group was tested using MTT. Real-time PCR was employed to detect the expression of connexin43 (Cx43) mRNA and western blot to detect the expression of Cx43 and phosphorylated Cx43. SD rats were randomly divided into the sham-operation group (SO). myocardial infarction group (MI). Irbesartan group and MI+ Irbesartan group, with 10 rats in each group. HE staining was employed to observe the change in the pathomorpholouy of left ventricular tissue and TUNEL method to analyze the cell apoptosis in the tissue. The immunofluorescence was adopted to observe the expression and distribution of Cx43 in the left ventricular myocardium and study the change in the expression of Cx43 in the cardiac muscular tissue at mRNA and protein level. Results: The intervention of lrbesartan in the condition of ischemia indicated the significant decrease in the number of necrotic cells. The expression of Cx43 was significantly decreased under the culture of ischemia (P<0.05), but in the presence of Irbesartan, the expression of Cx43 was increased compared with the ischemia group (p<0.01). The results of WB assay showed the similar trend of change at mRNA level. There was the significant difference in the score of ventricular arerythmia between MI group and SO group (P<0.01). The incidence of ventricular tachycardia or ventricular fibrillation was significantly increased compared with the one in SO group (P<0.05). There was the significant difference in the overall score between MI+Irbesartan group and MI group (P<0.05). The expression of Cx43 in the cardiac muscular tissue in MI group was significantly decreased (P<0.01(US) SO group). But the expression of Cx43 was increased after the treatment with Irbesartan. Conclusions: Irbesartan can inhibit the injury of H9c2 cardiomyocytes and the decreased expression of Cx43 that are induced by the ischemic myocardial infarction. Irbesartan can also improve the reconstruction of Cx43 in rats with ischemic myocardium to inhibit the myocardial infarction-induced arrhythmias.

Continuous Positive Airway Pressure Effectively Alleviates Arrhythmias in Patients with Obstructive Sleep Apnea:Possible Relationship with Counteracting Oxidative Stress

This work is aimed at exploring the clinical efficacy of continuous positive airway pressuie(CPAP)in treatment of patients with arrhythmias combined with obstructive sleep apnea(OSA).Through evaluating serum native thiol,malonaldehyde(MDA)and nicotinamide adenine dinucleotide phosphate oxidase(NADPH oxidase)in these patients and describing the effects on oxidative parameters of CPAP therapy for 3 months,we confirmed the impact of oxidative stress on arrhythmias.A total of 64 patients with OSA combined with arrhythmias were collected from April 2014 to April 2017 with full clinical information.Patients were divided into two groups(paired experiment design):32 patients in group A(control group),who received unchanged anti-arrhythmia treatment and 32 patients in group B,who were subjected to unchanged pharmacological anti-arrhythmia therapy combined with CPAP.OSA related parameters were compared between the two groups after 3-month therapy.And the levels of parameters of oxidative stress in patients were measured before and after CPAP therapy.After 3 months of CPAP therapy,compared with the control group,the percentage of sage N3(NREM 3)and stage R(REM)in total sleep time was significantly increased,while apnea-hypopnea index(AHI)and the Epworth Sleepiness Scale(ESS)score were evidently decreased.Meanwhile,the lowest oxygen saturation(LSpCh)was also elevated after CPAP treatment for 3 months.The CPAP therapy significantly prevented the occurrence of arrhythmias(P<0.05).Both the MDA level and NADPH oxidase levels were significantly lower in the group B than in the group A(P<0.05).But serum native thiol was improved by CPAP treatment(P<0.05).In conclusion,proper use of CPAP therapy provides significant benefits for the treatment of arrhythmia in patients with OSA.

Effects of antiarrhythmic peptide 10 on acute ventricular arrhythmia

Objective:To observe the effects antiarrhythmic peptide 10(AAPIO) aon acute ventricular arrhythmia and the phosphorylation state of ischemic myocardium conncxin.Methods:Acute total ischemia and partial ischemia models were established by ceasing perfusion and ligating the left anterior descending coronary artery in SD rats.The effects of AAP10(1 mg/L) on the incidence rate of ischemia-induced ventricular arrhythmia were observed.The ischemic myocardium was sampled to detect total-Cx43 and NP-Cx43 by immunofluorcsecnt staining and western blotting,the total-Cx43 expression was detected through image analysis system by semi-quantitative analysis.Results:AAP10 could significantly decrease the incidence of ischemia-induced ventricular tachycardia and ventricular fibrillation.During ischemic stage,total ischemia(TI) and AAP10 total ischemia(ATI) groups were compared with partial ischemia(Pi) and AAP10 partial ischemia(API) groups.The rates of incidence for arrhythmia in the ATI and API groups(10%and 0%) were lower than those in the TI and PI groups(60%and 45%).The difference between the two groups was statistically significant(P=0.019,P=0.020).The semi-quantitative analysis results of the ischemic myocardium showed that the total-Cx43 protein expression distribution areas for TI.ATI,PI and API groups were significantly decreased compared with the control group.On the other hand,the NP-Cx43 distribution areas of TI,ATI,PI and API groups were significantly increased compared with the control group(P>0.05).AAP10 could increase the total-Cx43 expression in the ischemic area and decrease the NP-Cx43 expression.Western blot results were consistent with the results of immunofluorescence staining.Conclusions:AAP10 can significantly decrease the rate of incidence of acute ischemia-induced ventricular tachycardia and ventricular fibrillation.Acute ischemic ventricular arrhythmias may have a relationship with the decreased phosphorylation of Cx43 induced by ischemia.AAP10 may stimulate the phosphorylation of Cx43 by increasing the totai-Cx43 expression and decreasing the NP-Cx43 expression in the ischemic area,so as to decrease ventricular arrhythmia.

Arrhythmia risk in liver cirrhosis

Interactions between the functioning of the heart and the liver have been described, with heart diseases affecting the liver, liver diseases affecting the heart, and conditions that simultaneously affect both. The heart is one of the most adversely affected organs in patients with liver cirrhosis. For example, arrhythmias and electrocardiographic changes are observed in patients with liver cirrhosis. The risk for arrhythmia is influenced by factors such as cirrhotic cardiomyopathy, cardiac ion channel remodeling, electrolyte imbalances,impaired autonomic function, hepatorenal syndrome, metabolic abnormalities, advanced age, inflammatory syndrome, stressful events, impaired drug metabolism and comorbidities. Close monitoring of cirrhotic patients is needed for arrhythmias, particularly when QT intervalprolonging drugs are given, or if electrolyte imbalances or hepatorenal syndrome appear. Arrhythmia risk may persist after liver transplantation due to possible QT interval prolongation, persistence of the parasympathetic impairment, post-transplant reperfusion and chronic immunosuppression, as well as consideration of the fact that the transplant itself is a stressful event for the cardiovascular system. The aims of the present article were to provide a review of the most important data regarding the epidemiology, pathophysiology, and biomarkers of arrhythmia risk in patients with liver cirrhosis, to elucidate the association with long-term outcome, and to propose future research directions.

Cardiac arrhythmias and cardiac arrest related to mushroom poisoning: A case report

BACKGROUND Mushroom exposure is a global health issue.The manifestations of mushroom poisoning (MP) may vary.Some species have been reported as rhabdomyolytic,hallucinogenic,or gastrointestinal poisons.Critical or even fatal MPs are mostly attributable to Amanita phalloides,with the development of severe liver or renal failure.Myocardial injury and even cases mimicking ST-segment elevation myocardial infarction (STEMI) have been previously reported,while cardiac arrhythmia or cardiac arrest is not commonly seen.CASE SUMMARY We report a 68-year-old woman with MP who suffered from delirium,seizure,long QT syndrome on electrocardiogram (ECG),severe cardiac arrhythmias of multiple origins,and cardiac arrest.She was intubated and put on blood perfusion.Her kidney and liver functions were intact;creatine kinase-MB was mildly elevated,and then fell within normal range during her hospital stay.We sent the mushrooms she left for translation elongation factor subunit 1α,ribosomal RNA gene sequence,and internal transcribed spacer sequence analyses.There were four kinds of mushrooms identified,two of which were found to be toxic.CONCLUSION This is the first time that we found cardiac toxicity caused by Panaeolus subbalteatus and Conocybe lactea,which were believed to be toxic to the liver,kidney,and brain.We suggest that intensive monitoring and ECG follow-up are essential to diagnose prolonged QT interval and different forms of tachycardia in MP patients,even without the development of severe liver or renal failure.The mechanisms need to be further investigated and clarified based on animal experiments and molecular signal pathways.

C-reactive protein as a predictor of malignant ventricular arrhythmias in non-ST elevation myocardial infarction

Objective To investigate whether C-reactive protein (CRP) is a biomarker of malignant ventricular arrhythmias (MVA) occurring in non-ST elevation myocardial infarction (NSTEMI) patients with Global Registry of Acute Coronary events (GRACE) scores < 140. Methods A total of 1450 NSTEMI patients were included in this study. Hs-CRP blood levels were measured via a turbidimetric immunoassay after confirming the diagnosis of NSTEMI with GRACE scores < 140. Results Consistent with prior studies, the MVA occurrence rate in our cohort was 6.7%, and patients with MVA exhibited a reduced left ventricular ejection fraction (46.1%± 6.9% vs. 61.5%± 8.7%, P = 0.032), a higher incidence of Killip classification > 1 (34.1% vs. 24.2%, P < 0.001), an increased surgical revascularization rate (34.1% vs. 9.7%, P < 0.001), and increased mortality (16.5% vs. 5.8%, P < 0.001). Serum hs-CRP levels were higher (P = 0.003) in NSTEMI patients with MVA, and this increase appeared unrelated to other clinical parameters. The C-statistic to discriminate MVA was 0.82 (95% CI: 0.74–0.89). Using receiver operating characteristics analysis, we optimized a cutoff point of 16 mL/L, and the sensitivity and specificity were 95% and 61%, respectively;the positive predictive value was 20% and the negative predictive value was 99%. Conclusions An hs-CRP assay is a potential MVA biomarker in low-risk NSTEMI patients with GRACE scores < 140. If validated in prospective studies, hs-CRP may offer a low-cost supplementary strategy for risk stratification for NSTEMI patients.

Elevated Platelet Activating Factor Level in Ischemia-Related Arrhythmia and Its Electrophysiological Effect on Myocardium

Objective The mechanism through which platelet activating factor （PAF） induces cardiac electrical activity and arrhythmia is not well understood and previous studies have suggested a potential involvement of ion channels in its action. The present study was aimed to clarify the role of PAF in fatal arrhythmias following acute myocardia infarction （AMI） and the underlying mechanism. Methods （1） Blood PAF levels were measured among 72 AMI patients at the time of diagnosis with AMI and 48 h later, and their electrocardiogram （ECG） was recorded continuously. （2） Ischemia simulation and surface electrocardiogram were conducted in 20 pigs and their PAF levels were measured. （3） PAF perfusion and standard microelectrode recording were performed on guinea pig papillarymuscles. Results In both humans and pigs, elevated PAF levels were detected in AMI and simulated ischemia, respectively, and even higher PAF levels were found when fatal arrhythmias occurred. In guinea pig myocardium, PAF induced a shortening of action potential duration at 90% level of repolarization （APD 90 ）under non-ischemic conditions and a more pronounced shortening under early simulated ischemic conditions. Conclusion AMI and ischemia are associated with increased PAF levels in humans and pigs, which are further raised when fatal arrhythmia follows. The effects of PAF on the myocardium may be mediated by multiple ion channels.

Associations between cardiac arrhythmia,incident disability in activities of daily living and physical performance:the ILSA study

Background Cardiac arrhythmias are common conditions in older people.Unfortunately,there is limited literature on associations between cardiac arrhythmias and physical performance or disability.We therefore aimed to prospectively investigate associations between cardiac arrhythmias and changes in disability and physical performance during 8 years of follow-up,using data from the Italian Longitudinal Study on Aging(ILSA).Methods Cardiac arrhythmias diagnosis was posed through a screening phase,confirmed by a physician.The onset of disability in activities of daily living(ADL)and the changes in several physical performance tests during follow-up were considered as outcomes.Fully-adjusted and propensity-score Cox Proportional Hazard models and mixed models were used for exploring associations between cardiac arrhythmia and the outcomes of interest.Results The prevalence of cardiac arrhythmia at baseline was 23.3%.People reporting cardiac arrhythmia at the baseline were significantly older,more frequently male,smokers and reported a higher presence of all medical conditions investigated(hypertension,heart failure,angina,myocardial infarction,diabetes,stroke),but no difference in dementia,Parkinsonism,cognitive or mood disorder.Cardiac arrhythmia at baseline was significantly associated with the incidence of disability in ADL(HR=1.23;95%:CI:1.01–1.50;P=0.0478 in propensity score analyses;HR=1.28;95%CI:1.01–1.61;P=0.0401 in fully adjusted models).Cardiac arrhythmia at baseline was also associated with a significant worsening in balance test(P=0.0436).Conclusions The presence of cardiac arrhythmia at baseline was associated with a significant higher risk of disability and of worsening in some physical performance tests,particularly those relating to balance.Screening and frequently assessing physical performance in older people affected by cardiac arrhythmia can be important to prevent a loss of physical performance,with further,potential,complications of medical management.

Cardiac Arrhythmia Disease Classication Using LSTM Deep Learning Approach

Many approaches have been tried for the classication of arrhythmia.Due to the dynamic nature of electrocardiogram(ECG)signals,it is challenging to use traditional handcrafted techniques,making a machine learning(ML)implementation attractive.Competent monitoring of cardiac arrhythmia patients can save lives.Cardiac arrhythmia prediction and classication has improved signicantly during the last few years.Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal,either faster or slower than normal.It is the most frequent cause of death for both men and women every year in the world.This paper presents a deep learning(DL)technique for the classication of arrhythmias.The proposed technique makes use of the University of California,Irvine(UCI)repository,which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes.In this research,our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset.The DL approach in the form of long short-term memory(LSTM)is an efcient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis.The goal of this research was to categorize cardiac arrhythmia patients by developing an efcient intelligent system using the LSTM DL algorithm.This approach to arrhythmia classication includes classication algorithms along with noise removal techniques.Therefore,we utilized principal components analysis(PCA)for noise removal,and LSTM for classication.This hybrid comprehensive arrhythmia classication approach performs better than previous approaches to arrhythmia classication.We attained a highest classication accuracy of 93.5%with the DL based disease classication system,and outperformed the earlier approaches used for cardiac arrhythmia classication.

Real-Time Patient-Specific ECG Arrhythmia Detection by Quantum Genetic Algorithm of Least Squares Twin SVM

The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm(QAG)based on least squares twin support vector machine(LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results over the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22%,99.65%and 99.41%for the abnormal,ventricular ectopic beats(VEBs)and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.