A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms

Content Provider	Scilit
Author	Sodmann, Philipp Vollmer, Marcus Nath, Neetika Kaderali, Lars
Copyright Year	2018
Description	Journal: Physiological Measurement Motivation: 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. Methods: 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 F1 scores by applying the algorithm to the hidden test set provided by the PhysioNet/CinC Challenge 2017. Results: The true positive rate of the convolutional neural network in detection of manually revised R peaks in the QT database was 98% and the positive predictive value was 99%. The detection of P and T waves reached a true positive rate of 92% and 88% respectively, given a 50 ms tolerance when comparing the reference to the test annotation set. The rhythm classification performance reached an overall F1 score of 0.82 when applying the algorithm to the hidden test se
Related Links	http://iopscience.iop.org/article/10.1088/1361-6579/aae304/pdf
ISSN	09673334
e-ISSN	13616579
DOI	10.1088/1361-6579/aae304
Journal	Physiological Measurement
Issue Number	10
Volume Number	39
Language	English
Publisher	IOP Publishing
Publisher Date	2018-09-20
Access Restriction	Open
Subject Keyword	Journal: Physiological Measurement Medical Informatics Convolutional Neural Network Deep Learning Ecg Annotation Heart Rhythm Machine Learning
Content Type	Text
Resource Type	Article
Subject	Physiology Physiology (medical) Biophysics Biomedical Engineering