Detection of exact emotions through multi-modal physiological signals provides relevant
information for different processes. Numerous computational approaches have been …

Introduction Emotion recognition plays a crucial role in affective computing. Recent studies
have demonstrated that the fuzzy boundaries among negative emotions make recognition …

Over the last decade, deep learning applications in biomedical research have exploded,
demonstrating their ability to often outperform previous machine learning approaches in …

Automatic analysis of electrocardiogram (ECG) signals is one of the applications in the
medical domain where deep learning methods demonstrate impressive performance …

Electrodermal activity (EDA) is a biosignal that contains valuable information for monitoring
health conditions related to sympathetic nervous system activity. Analyzing ambulatory EDA …

We present a novel application of contrastive learning technique in learning the feature
representation of ECG signal in a self-supervised manner for the classification of acute …

The timely psychological stress detection can improve the quality of human life by
preventing stress-induced behavioral and pathological consequences. This paper presents …

We learn to map the ECG signal to emotional or physical states using a 1D-CNN followed by
a Transformer. To overcome the limited number of samples, we propose a new self …

This paper presents a systematic investigation into the effectiveness of Self-Supervised
Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by …

Cardiovascular diseases are a significant cause of mortality worldwide, and the accurate
diagnosis of these conditions is essential for effective treatment and management …