Key result
A convolutional neural network combined with extreme gradient boosting trees achieved an overall F1 score of 0.82 for rhythm classification on the hidden test set of the PhysioNet/CinC Challenge 2017.
Why the study?
Does a convolutional neural network combined with extreme gradient boosting trees improve ECG annotation and rhythm classification accuracy compared to standard detectors?
Does a convolutional neural network combined with extreme gradient boosting trees improve ECG annotation and rhythm classification accuracy compared to standard detectors?
A deep learning approach using CNNs and extreme gradient boosting trees achieved an F1 score of 0.82 for ECG rhythm classification, ranking 9th in the PhysioNet/CinC Challenge 2017.
Supports ML-based ECG rhythm classification in benchmarks; leaves open prospective clinical validation before adoption.
Objective : Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our aim is to increase the accuracy of heart rhythm estimation by the use of extreme gradient boosting trees and the development of a deep convolutional neural network for ECG segmentation. Approach : We trained a convolutional neural network with waveforms from PhysioNet databases to annotate QRS complexes, P waves, T waves, noise and interbeat ECG segments that characterize the essences of normal and irregular heart beats. We evaluated true positive rates, positive predictive values and mean absolute differences of our annotation based on reference annotations of the QT and MIT-BIH P-wave database. Moreover, we compared the results with standard QRS detectors and Ecgpuwave. Extreme gradient boosting trees were used to determine the heart rhythm based on hand-crafted features. More precisely, a noise estimation function was used in combination with heart rate and interval data. Furthermore we defined particular features based on ECG morphology, appearance of P waves and detection of irregular beats. We examined the feature importance and identified key features for normal sinus rhythm, atrial fibrillation, alternative rhythm and noisy recordings. The classification performance was evaluated externally using F 1 scores by applying the algorithm to the hidden test set provided by the PhysioNet/CinC Challenge 2017. Main results : The true positive rate of the convolutional neural network in detection of manually revised R peaks in the QT database was and the positive predictive value was . The detection of P and T waves reached a true positive rate of and respectively, given a 50 ms tolerance when comparing the reference to the test annotation set. The rhythm classification performance reached an overall F 1 score of 0.82 when applying the algorithm to the hidden test set. Significance : We achieved a shared rank #9 in the post-challenge phase of the PhysioNet/CinC Challenge 2017.
No takes yet. Share an insight, caveat, or question.
Sodmann et al. (2018) studied Cardiovascular diseases, atrial fibrillation and other arrhythmias. Convolutional neural network and extreme gradient boosting trees vs. Standard QRS detectors and Ecgpuwave was evaluated on Rhythm classification performance (overall F1 score). A convolutional neural network combined with extreme gradient boosting trees achieved an overall F1 score of 0.82 for rhythm classification on the hidden test set of the PhysioNet/CinC Challenge 2017.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: