This article introduces a systematic review on arousal classification based on electrodermal
activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six …

With the aid of advancing technologies, the healthcare sector is improving more swiftly than
ever, yet there are still many unexplored areas and there is always potential for …

Emotion recognition may establish a clinical framework for measuring emotional wellbeing
and screening for quality of life, cognitive dysfunction, and mental disorder. Emotions are …

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the
application of which in electrocardiographic signals is gaining importance. So far, limited …

As Internet of Things (IoT) networks expand globally with an annual increase of active
devices, providing better safeguards to threats is becoming more prominent. An intrusion …

In this paper performance of KNN models are evaluated by changing K-fold cross validation
parameter and total number of nearest neighbors while classifying WESAD dataset using …

We propose cross-modal attentive connections, a new dynamic and effective technique for
multimodal representation learning from wearable data. Our solution can be integrated into …

The article deals with the detection of stress using the electrodermal activity (EDA) signal
measured at the wrist. We present an approach for feature extraction from EDA. The …

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have
demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung …

Continuous and long-term measurement of physiological signals out of clinical settings may
face different processing requirements resulting in higher costs or reduced performance …