Self-supervised representation learning from 12-lead ECG data

by   Temesgen Mehari, et al.

We put forward a comprehensive assessment of self-supervised representation learning from short segments of clinical 12-lead electrocardiography (ECG) data. To this end, we explore adaptations of state-of-the-art self-supervised learning algorithms from computer vision (SimCLR, BYOL, SwAV) and speech (CPC). In a first step, we learn contrastive representations and evaluate their quality based on linear evaluation performance on a downstream classification task. For the best-performing method, CPC, we find linear evaluation performances only 0.8 analyze the impact of self-supervised pretraining on finetuned ECG classifiers as compared to purely supervised performance and find improvements in downstream performance of more than 1 increased robustness against physiological noise. All experiments are carried out exclusively on publicly available datasets, the to-date largest collection used for self-supervised representation learning from ECG data, to foster reproducible research in the field of ECG representation learning.


Speech Representation Learning Through Self-supervised Pretraining And Multi-task Finetuning

Speech representation learning plays a vital role in speech processing. ...

Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

We release the largest public ECG dataset of continuous raw signals for ...

Learning ECG Representations based on Manipulated Temporal-Spatial Reverse Detection

Learning representations from electrocardiogram (ECG) serves as a fundam...

Taxonomy of multimodal self-supervised representation learning

Sensory input from multiple sources is crucial for robust and coherent h...

Learning Generalizable Physiological Representations from Large-scale Wearable Data

To date, research on sensor-equipped mobile devices has primarily focuse...

Probing the State of the Art: A Critical Look at Visual Representation Evaluation

Self-supervised research improved greatly over the past half decade, wit...

Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes

Vast quantities of person-generated health data (wearables) are collecte...