5 papers · 1 filter
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
William Lehn-Schiøler, Magnus Ruud Kjær, Rahul Thapa +10
EEG foundation models achieve state-of-the-art clinical performance, yet the internal computations driving their predictions remain opaque: a barrier to clinical trust. We apply To…
Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation
Adrian Atienza, Gouthamaan Manimaran, Sadasivan Puthusserypady +3
The integration of Artificial Intelligence (AI) into clinical research has great potential to reveal patterns that are difficult for humans to detect, creating impactful connection…
Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA
Adtian Atienza, Jakob E. Bardram, Sadasivan Puthusserypady
Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such as Self-Supervised Learning (SSL)…
CuPID: Leveraging Masked Single-Lead ECG Modelling for Enhancing the Representations
Adtian Atienza, Gouthamaan Manimaran, Jakob E. Bardram +1
Wearable sensing devices, such as Electrocardiogram (ECG) heart-rate monitors, will play a crucial role in the future of digital health. This continuous monitoring leads to massive…
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series
Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
Despite recent advancements in Self-Supervised Learning (SSL) for time series analysis, a noticeable gap persists between the anticipated achievements and actual performance. While…