Effectiveness of Transfer Learning for Deep Learning-Based Electrocardiogram Analysis.

ABSTRACT:

Objectives

Many deep learning-based predictive models evaluate the waveforms of electrocardiograms (ECGs). Because deep learning-based models are data-driven, large and labeled biosignal datasets are required. Most individual researchers find it difficult to collect adequate training data. We suggest that transfer learning can be used to solve this problem and increase the effectiveness of biosignal analysis.

Methods

We applied the weights of a pretrained model to another model that performed a different task (i.e., transfer learning). We used 2,648,100 unlabeled 8.2-second-long samples of ECG II data to pretrain a convolutional autoencoder (CAE) and employed the CAE to classify 12 ECG rhythms within a dataset, which had 10,646 10-second-long 12-lead ECGs with 11 rhythm labe

SUBMITTER: Jang JH 

PROVIDER: S-EPMC7921576 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature