Key result
A 2D convolutional neural network using time-frequency feature maps from standard 12-lead ECGs classified eight types of cardiac arrhythmias with a weighted F1 score of 0.78 and a weighted AUC-ROC of 0.87.
Why the study?
Abnormal signs of cardiac arrhythmia may not appear in all ECG leads, necessitating comprehensive 12-channel analysis to accurately diagnose persistent and episodic arrhythmias.
Does a 2D CNN model using time-frequency feature maps accurately classify eight types of arrhythmias from standard 12-lead ECGs?
Does a 2D CNN model using time-frequency feature maps accurately classify eight types of arrhythmias from standard 12-lead ECGs?
A 2D CNN model using time-frequency feature maps from 12-lead ECGs can effectively classify persistent arrhythmias, though performance on episodic arrhythmias needs improvement.
Proposed ECG analysis approach may aid arrhythmia detection; leaves open clinical utility versus standard 12-lead review.
Electrocardiograms (ECGs) are widely used for diagnosing cardiac arrhythmia based on the deformation of signal shapes due to changes in various heart diseases. However, these abnormal signs may not be observed in some 12 ECG channels, depending on the location, the heart shape, and the type of cardiac arrhythmia. Therefore, it is necessary to closely and comprehensively observe ECG records acquired from 12 channel electrodes to diagnose cardiac arrhythmias accurately. In this study, we proposed a clustering algorithm that can classify persistent cardiac arrhythmia as well as episodic cardiac arrhythmias using the standard 12-lead ECG records and the 2D CNN model using the time-frequency feature maps to classify the eight types of arrhythmias and normal sinus rhythm. The standard 12-lead ECG records were provided by China Physiological Signal Challenge 2018 and consisted of 6877 patients. The proposed algorithm showed high performance in classifying persistent cardiac arrhythmias; however, its accuracy was somewhat low in classifying episodic arrhythmias. If our proposed model is trained and verified using more clinical data, we believe it can be used as an auxiliary device for diagnosing cardiac arrhythmias.
No takes yet. Share an insight, caveat, or question.
Jeong et al. (2021) studied Cardiac arrhythmia (n=6,877). 2D Convolutional Neural Network (CNN) with time-frequency feature maps was evaluated on Weighted F1 score for arrhythmia classification. A 2D convolutional neural network using time-frequency feature maps from standard 12-lead ECGs classified eight types of cardiac arrhythmias with a weighted F1 score of 0.78 and a weighted AUC-ROC of 0.87.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: