AIMC Topic: Arrhythmias, Cardiac

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A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length.

Physiological measurement Mar 27, 2018
OBJECTIVE: Atrial fibrillation (AF) is a major cause of hospitalization and death in the United States. Moreover, as the average age of individuals increases around the world, early detection and diagnosis of AF become even more pressing. In this pap...
Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Humans Neural Networks, Computer
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Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

Computational and mathematical methods in medicine Mar 5, 2018
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 sixte...
Humans Datasets as Topic Cardiac Conduction System Disease Support Vector Machine Arrhythmias, Cardiac
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Automatic QRS complex detection using two-level convolutional neural network.

Biomedical engineering online Jan 29, 2018
BACKGROUND: The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and paramete...
Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Algorithms Sensitivity and Specificity Models, Theoretical Humans Signal-To-Noise Ratio Neural Networks, Computer
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Quantitative Assessment of the Physiological Parameters Influencing QT Interval Response to Medication: Application of Computational Intelligence Tools.

Computational and mathematical methods in medicine Jan 4, 2018
Human heart electrophysiology is complex biological phenomenon, which is indirectly assessed by the measured ECG signal. ECG trace is further analyzed to derive interpretable surrogates including QT interval, QRS complex, PR interval, and T wave morp...
Algorithms Reproducibility of Results Electrocardiography Humans Artificial Intelligence Software Heart Potassium Anti-Arrhythmia Agents Calcium Action Potentials Models, Statistical Regression Analysis Clinical Trials as Topic Cell Membrane Risk Electrophysiology Terfenadine Programming Languages Myocytes, Cardiac Observer Variation Arrhythmias, Cardiac Ions Sodium
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Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks.

BMC bioinformatics Dec 28, 2017
BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrh...
Ether-A-Go-Go Potassium Channels Torsades de Pointes Arrhythmias, Cardiac Myocytes, Cardiac Models, Cardiovascular Action Potentials Heart Ventricles Long QT Syndrome Humans Neural Networks, Computer ROC Curve Electrocardiography Male Middle Aged
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A deep convolutional neural network model to classify heartbeats.

Computers in biology and medicine Aug 24, 2017
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrh...
Databases, Factual Female Humans Neural Networks, Computer Myocardial Contraction Male Arrhythmias, Cardiac Electrocardiography Heart Rate Signal Processing, Computer-Assisted Models, Cardiovascular
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Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification.

Scientific reports Jul 20, 2017
Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of ...
Signal Processing, Computer-Assisted Arrhythmias, Cardiac Wavelet Analysis Electrocardiography Support Vector Machine Principal Component Analysis Humans Algorithms Reproducibility of Results
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Premature ventricular contraction detection combining deep neural networks and rules inference.

Artificial intelligence in medicine Jun 9, 2017
Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpa...
Humans Sensitivity and Specificity Arrhythmias, Cardiac Neural Networks, Computer Electrocardiography Ventricular Premature Complexes Algorithms Databases, Factual
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Patient-Specific Deep Architectural Model for ECG Classification.

Journal of healthcare engineering May 7, 2017
Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional E...
Wavelet Analysis Humans Neural Networks, Computer Electrocardiography Patient-Centered Care Signal Processing, Computer-Assisted Arrhythmias, Cardiac
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Reducing false arrhythmia alarm rates using robust heart rate estimation and cost-sensitive support vector machines.

Physiological measurement Jan 18, 2017
To lessen the rate of false critical arrhythmia alarms, we used robust heart rate estimation and cost-sensitive support vector machines. The PhysioNet MIMIC II database and the 2015 PhysioNet/CinC Challenge public database were used as the training d...
Costs and Cost Analysis Clinical Alarms Arrhythmias, Cardiac Heart Rate False Positive Reactions Humans Electrocardiography Time Factors Support Vector Machine Signal Processing, Computer-Assisted
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