The use of heart rate variability (HRV) in research has been greatly popularized over the
past decades due to the ease and affordability of HRV collection, coupled with its clinical …

The heart-rate dynamics are one of the most analyzed physiological interactions. Many
mathematical methods were proposed to evaluate heart-rate variability. These methods …

Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …

Recently, development of wearable and Internet of Things (IoT) technologies enables the
real-time and continuous individual electrocardiogram (ECG) monitoring. In this paper, we …

The number of people with a chronic disease is rapidly increasing, giving the healthcare
industry more challenging problems. To date, there exist several ontology and IoT-based …

Background: Emotion is composed of cognitive processing, physiological response and
behavioral reaction. Heart rate variability (HRV) refers to the fluctuations between …

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of
biomedical time series. Recent developments in the field have tried to alleviate the problem …

Atrial fibrillation (AFib) is diagnosed by analysis of the morphological and rhythmic
properties of the electrocardiogram. It was recently shown that accurate detection of AFib is …

Alzheimer's disease (AD) is the most prevalent form of dementia in the world, which is
characterised by the loss of neurones and the build-up of plaques in the brain, causing …

Biomedical signals are frequently noisy and incomplete. They produce complex and high-
dimensional data sets. In these mentioned cases, the results of traditional methods of signal …