Showing eess.SPShow all
3 papers · 1 filter
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…
eess.SP2024
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis
Gouthamaan Manimaran, Sadasivan Puthusserypady, Helena Domínguez +2
Electrocardiogram (ECG) signals are critical for diagnosing heart conditions and capturing detailed cardiac patterns. As wearable single-lead ECG devices become more common, effici…
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…