Ensemble classification combining ResNet and handcrafted features with three-steps training

Abstract

Objective.This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to integrate deep and classic machine learning features into a single model, exploring the proper structure and training procedure.Approach.From the initial 88 253 signals, only 84 210 were included. Low quality and unscored recordings were excluded. Three different database subsets of 40 365 recording each were created by dividing in three normal sinus rhythm and sinus bradycardia recordings. Each subset was used to train a different model with shared architecture integrated as an ensemble to provide the final classification through major voting. Models contained a deep branch composed of a modified ResNet with dilation convolutional layers and squeeze and excitation Block that took as input windowed ECG signals. This was concatenated with a wide branch that integrated 20 cardiac rhythm features into a fully connected 3-layered network. Three different training steps were studied: just the deep branch (D), wide integration and training (D+W), and a final fine tuning of the deep branch posterior to wide training (D+W+D).Main Results.Results obtained in a local test set formed by a stratified 12.5% split of the given full dataset were presented for 2-lead and 12-lead models. The best training method was the 3-step D + W + D procedure obtaining a challenge metric of 0.709 and 0.677 for 12 and 2-lead models respectively.Significance.Integration of handcrafted features and deep learning model not only may increase the generalization capacity of the network but also provide a path to add explicit information into the classification decision process. To the best of our knowledge this is the first work studying the training procedure to properly integrate both types of information for ECG signals classification.

Keywords: deep learning; electrocardiogram; electrophysiology; machine learning.