In Physiological measurement ; h5-index 36.0
OBJECTIVE : The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the PhysioNet/Computing in Cardiology Challenge 2021. The training set is a public database of 88253 twelve-lead ECG recordings lasting from 6 seconds to 60 seconds. Each ECG recording has one or more diagnostic labels. The six-lead, four-lead, three-lead, and two-lead are reduced-lead versions of the original twelve-lead data.
APPROACH : The deep learning method considers images that are built from raw ECG signals. This technique considers innovative 3-D display of the entire ECG signal, observing the regional constraints of the leads, obtaining time-spatial images of the 12 leads, where the x-axis is the temporal evolution of ECG signal, the y-axis is the spatial location of the leads, and the z-axis (color) the amplitude. These images are used for training Convolutional Neural Networks with GoogleNet for ECG diagnostic classification.
MAIN RESULTS : The official results of the classification accuracy of our team named 'Gio_new_img' received scores of 0.4, 0.4, 0.39, 0.4 and 0.4 (ranked 18th, 18th, 18th,18th, 18th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set with the Challenge evaluation metric.
SIGNIFICANCE : The results indicated that all these algorithms have similar behaviour in the various lead groups, and the most surprising and interesting point is the fact that the 2-lead scores are similar to those obtained with the analysis of 12 leads. It permitted to test the diagnostic potential of the reduced-lead ECG recordings. These aspects can be related to the pattern recognition capacity and generalizability of the deep learning approach and/or to the fact that the characteristics of the considered cardiac abnormalities can be extracted also from a reduced set of leads.
Bortolan Giovanni
2023-Jan-19
ECG, convolutional neural network, diagnostic classification, temporal-space images
