Contrastive Self-Supervised Learning for Stress Detection from ECG Data
In recent literature, ECG-based stress assessment has become popular due to its proven correlation to stress and increased accessibility of ECG data through commodity hardware. However, most ECG-based stress assessment models use supervised learning, relying on manually-annotated data. Limited resea...
Main Authors: | Suha Rabbani, Naimul Khan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
|
Series: | Bioengineering |
Subjects: | |
Online Access: | https://www.mdpi.com/2306-5354/9/8/374 |
Similar Items
-
Specific Emitter Identification Based on Self-Supervised Contrast Learning
by: Bo Liu, et al.
Published: (2022-09-01) -
SSMDA: Self-Supervised Cherry Maturity Detection Algorithm Based on Multi-Feature Contrastive Learning
by: Rong-Li Gai, et al.
Published: (2023-04-01) -
A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection
by: Jing Zhou, et al.
Published: (2023-01-01) -
Rethinking Pseudo-Labeling for Semi-Supervised Facial Expression Recognition With Contrastive Self-Supervised Learning
by: Bei Fang, et al.
Published: (2023-01-01) -
Deep Contrastive Learning-Based Model for ECG Biometrics
by: Nassim Ammour, et al.
Published: (2023-02-01)