11 papers
Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG
Frederik Møllskov Trier, Xiaopeng Mao, Sadasivan Puthusserypady
Imagined speech decoding from electroencephalography (EEG) has gained increasing attention as a potential communication pathway for individuals with severe motor impairments, yet r…
Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach
Xiaopeng Mao, Marike Weisbjerg, Sadasivan Puthusserypady
This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG).…
FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection
Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk +5
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality. Recent ECG foundation model…
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…
RAG-based EEG-to-Text Translation Using Deep Learning and LLMs
Enrico Collautti, Xiaopeng Mao, Luca Tonin +2
The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (BCI) research. In particular,…
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…
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…
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…
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…