This study presents PhysioNauts Team's contribution to the PhysioNet/CinC Challenge 2021 on ECG classification for variable leads. Three types of labels were identified: those affecting cardiac rhythm, ECG morphology or both. The full model integrated handcrafted rhythm features and deep learning features into a residual neural network (ResNet) with a squeeze and excitation module and a wide 10-meuron single-layer fully connected (FC) branch to leverage the learning of both feature types. The ResNet inputs were ECG segments of 4096 samples downsampled to 257 Hz. The FC inputs were standard rhythm features extracted from the RR-series. Class imbalance was mitigated by selecting only a third of normal sinus rhythm and sinus bradycardia recordings. Moreover, threshold optimization was performed based on a grid search and the Nelder-Mead method to maximize the Challenge metric (CM). Our entry failed on the UMich test data, so it was not officially ranked and it didn't receive official scores on the full test set. The CMs obtained in the unofficial entry were 0.613, 0.585, 0.603, 0,594, and 0.582 on l2-lead, 6-lead, 4-lead, 3 -lead, 2 -lead, respectively.

Combining ResNet Model with Handcrafted Temporal Features for ECG Classification with Varying Number of Leads

Isla Garcia
2021

Abstract

This study presents PhysioNauts Team's contribution to the PhysioNet/CinC Challenge 2021 on ECG classification for variable leads. Three types of labels were identified: those affecting cardiac rhythm, ECG morphology or both. The full model integrated handcrafted rhythm features and deep learning features into a residual neural network (ResNet) with a squeeze and excitation module and a wide 10-meuron single-layer fully connected (FC) branch to leverage the learning of both feature types. The ResNet inputs were ECG segments of 4096 samples downsampled to 257 Hz. The FC inputs were standard rhythm features extracted from the RR-series. Class imbalance was mitigated by selecting only a third of normal sinus rhythm and sinus bradycardia recordings. Moreover, threshold optimization was performed based on a grid search and the Nelder-Mead method to maximize the Challenge metric (CM). Our entry failed on the UMich test data, so it was not officially ranked and it didn't receive official scores on the full test set. The CMs obtained in the unofficial entry were 0.613, 0.585, 0.603, 0,594, and 0.582 on l2-lead, 6-lead, 4-lead, 3 -lead, 2 -lead, respectively.
Computing in Cardiology
978-1-6654-7916-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1208498
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