Classification of Arrhythmia

Samad, Saleha; Khan, Shoab Ahmed; Haq, Anam; Riaz, Amna

doi:10.12720/ijoee.2.1.57-61

Cited by 17 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…tachycardia, bradycardia, or irregular) and poor cardiac function [5]. They monitor heart rhythm continuously and, when necessary, impulses are delivered to restore normal heart function [23]. In this systematic review, we attempted to assess the effect of using an electronic apex locator during root canal treatment on these devices.…”

Section: Discussionmentioning

confidence: 99%

Influence and safety of electronic apex locators in patients with cardiovascular implantable electronic devices: a systematic review

Alrahabi

Ghabbani

2018

Libyan Journal of Medicine

View full text Add to dashboard Cite

The widespread use of cardiovascular implantable electronic devices has increased concerns regarding using electronic apex locators in patients with these devices. This systematic review investigated the effects and safety of using electronic apex locators in patients with cardiovascular implantable electronic devices.Methods: An electronic search in the Cochrane Library, PubMed (MEDLINE), ScienceDirect, and Scientific Electronic Library Online (Scielo) databases for relevant articles published between December 2000 and December 2018 was performed. The search strategy centered on terms related to electronic apex locators use during root canal treatment in patients with cardiovascular implantable electronic devices.Results: Seven studies (five in vitro and two in vivo) fulfilled the inclusion criteria for this review. It was found that electronic apex locators can be used safely in patients with cardiovascular implantable electronic devices, when general precautions are followed.Conclusions: Although the present review suggests that electronic apex locators can be used safely in patients with implantable cardioverter defibrillators, consultation with patients’ cardiologists remains advisable.

show abstract

Section: Discussionmentioning

confidence: 99%

Influence and safety of electronic apex locators in patients with cardiovascular implantable electronic devices: a systematic review

Alrahabi

Ghabbani

2018

Libyan Journal of Medicine

View full text Add to dashboard Cite

show abstract

“…In [37], numerous machine learning models were discussed which constitutes even the neural networks, gradient boosting, random forest and SVM which are used for classification based on the application of rigorous preprocessing and feature selection technique for the ECG data. Similarly, the SVM, KNN, Logistic regression, decision trees, OneR, Naïve Bayes and J48 are used in the studies for different classes used to classify the arrhythmia [38], [39], [40].…”

Section: Related Workmentioning

confidence: 99%

Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

2019

IJEAT

View full text Add to dashboard Cite

Computer aided predictive analytics are vital in noncommunicable diseases. In particular, early diagnosis of arrhythmia (heart related disease) is crucial to prevent sudden deaths due to heart failure. The critical context to prevent deaths caused by arrhythmia is early prediction of the arrhythmia scope. The clinical experts widely consider the Electro Cardio Gram (ECG) report as primary parameter to scale the scope of arrhythmia. However, the diagnosis accuracy of clinical experts is highly correlate on their expertise. Unlike the other domains, the sensitivity that is the accuracy in disease-prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more vital in computer-aided heart disease prediction methods. Hence, the recent research contributions are quantifying the possibilities of optimizing machine-learning approaches to achieve significance in computer-aided methods to perform predictive analysis on arrhythmia detection. Regarding this context, this manuscript is defining a Regression Heuristics by Tridimensional Features of the electrocardiogram reports, which has intended to perform arrhythmia prediction. The experimental study evincing the significance of the proposed model that scaled against the contemporary methods.

show abstract

“…In [ 18 ], various machine learning algorithms including neural networks, decision trees, random forest, gradient boosting, and SVM are used for arrhythmia classification after applying rigorous preprocessing and feature selection techniques on ECG data. Similarly, OneR, J48, Naïve Bayes, SVM, logistic regression, KNN, random forest, and decision trees have been applied on ECG medical dataset to classify arrhythmia into 16 different classes [ 19 – 21 ]. Significant work on ECG dataset is conducted presenting SVM based methods for detecting arrhythmia with selection of significant features using principal component analysis [ 22 , 23 ].…”

Section: Arrhythmia Classification Modelsmentioning

confidence: 99%

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

Mustaqeem

Anwar

Majid

2018

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

show abstract

Classification of Arrhythmia

Cited by 17 publications

References 4 publications

Influence and safety of electronic apex locators in patients with cardiovascular implantable electronic devices: a systematic review

Influence and safety of electronic apex locators in patients with cardiovascular implantable electronic devices: a systematic review

Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

Contact Info

Product

Resources

About