AIMC Topic: Arrhythmias, Cardiac

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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 ...
Humans Algorithms Reproducibility of Results Signal Processing, Computer-Assisted Arrhythmias, Cardiac Wavelet Analysis Electrocardiography Support Vector Machine Principal Component Analysis
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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...
Electrocardiography Arrhythmias, Cardiac Ventricular Premature Complexes Humans Neural Networks, Computer Algorithms Databases, Factual Sensitivity and Specificity
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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...
Neural Networks, Computer Electrocardiography Patient-Centered Care Humans Signal Processing, Computer-Assisted Arrhythmias, Cardiac Wavelet Analysis
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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...
Humans Time Factors Electrocardiography Clinical Alarms Heart Rate False Positive Reactions Costs and Cost Analysis Support Vector Machine Signal Processing, Computer-Assisted Arrhythmias, Cardiac
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A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach.

Australasian physical & engineering sciences in medicine Nov 4, 2016
In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibril...
Tachycardia, Ventricular Ventricular Fibrillation ROC Curve Electrocardiography Fuzzy Logic Heart Ventricles Arrhythmias, Cardiac Databases as Topic Humans Algorithms
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Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals.

Physiological measurement Jul 25, 2016
False arrhythmia alarms pose a major threat to the quality of care in today's ICU. Thus, the PhysioNet/Computing in Cardiology Challenge 2015 aimed at reducing false alarms by exploiting multimodal cardiac signals recorded by a patient monitor. False...
Electrocardiography False Positive Reactions Monitoring, Physiologic Clinical Alarms Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Heart Rate Machine Learning Intensive Care Units
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Reduction of false arrhythmia alarms using signal selection and machine learning.

Physiological measurement Jul 25, 2016
In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy en...
Machine Learning Clinical Alarms Electrocardiography Monitoring, Physiologic Critical Care False Positive Reactions Signal-To-Noise Ratio Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Humans
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Suppression of false arrhythmia alarms in the ICU: a machine learning approach.

Physiological measurement Jul 25, 2016
This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of ...
Clinical Alarms Pattern Recognition, Automated False Positive Reactions Intensive Care Units Electrocardiography Monitoring, Physiologic Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Humans Machine Learning
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Efficient Fine Arrhythmia Detection Based on DCG P-T Features.

Journal of medical systems May 27, 2016
Due to the high mortality associated with heart disease, there is an urgent demand for advanced detection of abnormal heart beats. The use of dynamic electrocardiogram (DCG) provides a useful indicator of heart condition from long-term monitoring tec...
Signal Processing, Computer-Assisted Electrocardiography, Ambulatory Arrhythmias, Cardiac Humans Algorithms Support Vector Machine
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Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier.

Journal of medical systems Feb 27, 2016
In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of ...
Electrocardiography Decision Trees Principal Component Analysis Arrhythmias, Cardiac Heart Rate Wavelet Analysis Databases, Factual ROC Curve Image Processing, Computer-Assisted Humans Machine Learning
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