A convolution neural network model achieved a median overall F1-score of 0.84 for the nine-type classification of cardiac arrhythmias on a hidden test set of 2,954 ECG recordings.
Does a convolution neural network model accurately detect and classify cardiac arrhythmias from ECG recordings?
A deep learning convolutional neural network model demonstrated high accuracy (F1-score 0.84) in detecting and classifying nine types of cardiac arrhythmias from ECG recordings, with single-lead data performing nearly as well as 12-lead data.
Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations.
Chen et al. (Thu,) conducted a other in Cardiac arrhythmias (n=9,831). Convolution neural network model was evaluated on Nine-type cardiac arrhythmia classification (median overall F1-score). A convolution neural network model achieved a median overall F1-score of 0.84 for the nine-type classification of cardiac arrhythmias on a hidden test set of 2,954 ECG recordings.