Analysis of Augmentations for Contrastive ECG Representation Learning
This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-supervised framework consists of two main parts: the contrastive learning and the downstream task. In the first stage, we train an encoder using a number of augmentations to extract generalizable ECG signal representations. We then freeze the encoder and finetune a few linear layers with different amounts of labelled data for downstream arrhythmia detection. We then experiment with various augmentations techniques and explore a range of parameters. Our experiments are done on PTB-XL, a large and publicly available 12-lead ECG dataset. The results show that applying augmentations in a specific range of complexities works better for self-supervised contrastive learning. For instance, when adding Gaussian noise, a sigma in the range of 0.1 to 0.2 achieves better results, while poor training occurs when the added noise is too small or too large (outside of the specified range). A similar trend is observed with other augmentations, demonstrating the importance of selecting the optimum level of difficulty for the added augmentations, as augmentations that are too simple will not result in effective training, while augmentations that are too difficult will also prevent the model from effective learning of generalized representations. Our work can influence future research on self-supervised contrastive learning on bio-signals and aid in selecting optimum parameters for different augmentations.
Code (0)
등록된 구현이 없습니다.
Tasks
Arrhythmia DetectionContrastive LearningRepresentation LearningSelf-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmentations of different inputs. Recent attemp…
Contrastive LearningSelf-Supervised LearningTime Series Contrastive Learning with Information-Aware Augmentations
Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key…
Contrastive LearningOpen-Ended Question AnsweringRepresentation LearningTime SeriesInformation-Aware Time Series Meta-Contrastive Learning
Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key…
Contrastive LearningMeta-LearningOpen-Ended Question AnsweringRepresentation Learning+2Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions
In this paper, we present an empirical study of typical spatial augmentation techniques used in self-supervised representation learning methods (both contrastive and non-contrastive), namely random crop and cutout. Our c…
Representation LearningAutomatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning
Contrastive learning enables learning useful audio and speech representations without ground-truth labels by maximizing the similarity between latent representations of similar signal segments. In this framework various …
Contrastive LearningData AugmentationRepresentation LearningSpeech Representation Learning