Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

• URL: http://arxiv.org/abs/2212.04902v1
• Date: Wed, 7 Dec 2022 19:02:45 GMT
• Title: Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
• Authors: Ramin Ghorbani, Marcel T.J. Reinders, and David M.J. Tax
• Abstract summary: We propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. Results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial. SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes.
• Score: 3.8036939971290007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. However, supervised learning is often limited by the amount of available labeled data, which is typically expensive to obtain. To address this problem, we propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. The performance of the proposed SSL framework is compared with two fully supervised baselines. The results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial, and a simple classifier trained on SSL-learned representations outperforms fully supervised deep neural networks. However, the results reveal that the SSL-learned representations are too focused on encoding the subjects. Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce. The high inter-subject variability suggests that there is still room for improvements in learning representations. In general, the results suggest that SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes.

Related papers

• A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification [51.35500308126506]
  Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. We study how classification-based evaluation protocols for SSL correlate and how well they predict downstream performance on different dataset types.
  arXiv  Detail & Related papers  (2024-07-16T23:17:36Z)
• Boosting Transformer's Robustness and Efficacy in PPG Signal Artifact Detection with Self-Supervised Learning [0.0]
  This study addresses the underutilization of abundant unlabeled data by employing self-supervised learning (SSL) to extract latent features from this data. Our experiments demonstrate that SSL significantly enhances the Transformer model's ability to learn representations. This approach holds promise for broader applications in PICU environments, where annotated data is often limited.
  arXiv  Detail & Related papers  (2024-01-02T04:00:48Z)
• Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling [26.444935219428036]
  Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. In real-world settings, spurious correlations between some attributes (e.g. race, gender and age) and labels for downstream tasks often exist. We propose a learning-speed aware SSL (LA-SSL) approach, in which we sample each training data with a probability that is inversely related to its learning speed.
  arXiv  Detail & Related papers  (2023-11-27T22:52:45Z)
• CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking [11.616031590118014]
  CroSSL allows for handling missing modalities and end-to-end cross-modal learning. We evaluate our method on a wide range of data, including motion sensors.
  arXiv  Detail & Related papers  (2023-07-31T17:10:10Z)
• Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition [2.2082422928825136]
  Self-supervised learning (SSL) frameworks have been extensively applied to sensor-based Human Activity Recognition (HAR) In this paper, we aim to analyze deep representations of two recent SSL frameworks, namely SimCLR and VICReg.
  arXiv  Detail & Related papers  (2023-04-14T07:53:59Z)
• Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning [1.0499611180329804]
  Self-supervised learning (SSL) is used to evaluate the quality of visual representations learned via self-supervised learning (SSL) Existing SSL methods have shown good performances under those evaluation protocols. We try to figure out the cause of performance sensitivity by conducting extensive experiments with state-of-the-art SSL methods.
  arXiv  Detail & Related papers  (2023-04-07T03:03:19Z)
• A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends [82.64268080902742]
  Self-supervised learning (SSL) aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions.
  arXiv  Detail & Related papers  (2023-01-13T14:41:05Z)
• Self Supervised Learning for Few Shot Hyperspectral Image Classification [57.2348804884321]
  We propose to leverage Self Supervised Learning (SSL) for HSI classification. We show that by pre-training an encoder on unlabeled pixels using Barlow-Twins, a state-of-the-art SSL algorithm, we can obtain accurate models with a handful of labels.
  arXiv  Detail & Related papers  (2022-06-24T07:21:53Z)
• Self-supervised Learning is More Robust to Dataset Imbalance [65.84339596595383]
  We investigate self-supervised learning under dataset imbalance. Off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. We devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets.
  arXiv  Detail & Related papers  (2021-10-11T06:29:56Z)
• Graph-based Semi-supervised Learning: A Comprehensive Review [51.26862262550445]
  Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to naturally represent data as graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large scale data.
  arXiv  Detail & Related papers  (2021-02-26T05:11:09Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.