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The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.
With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes.
In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition.
Similarly, the convolutional neural network scored 72. 1% on the augmented database and 83% on the test set.
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system.
Access to electronic health record (EHR) data has motivated computational advances in medical research.
Ranked #1 on Arrhythmia Detection on The PhysioNet Computing in Cardiology Challenge 2017 (Accuracy (TRAIN-DB) metric)
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor.
We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification.
Ranked #1 on Time Series Classification on Physionet 2017 Atrial Fibrillation (F1 (Hidden Test Set) metric)
The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias.
Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests.