3 papers
eess.SP2024
An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation
Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
Deep Learning (DL) methods have been used for electrocardiogram (ECG) processing in a wide variety of tasks, demonstrating good performance compared with traditional signal process…
cs.LG2023
Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning
Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
By identifying similarities between successive inputs, Self-Supervised Learning (SSL) methods for time series analysis have demonstrated their effectiveness in encoding the inheren…
eess.SP2023
Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing
Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
Extracting information from the electrocardiography (ECG) signal is an essential step in the design of digital health technologies in cardiology. In recent years, several machine l…