Nonlinear dynamics and chaos theory have been used in neurophysiology with the aim to
understand the complex brain activity from electroencephalographic (EEG) signals …

Recently, bio-signal based control has been gradually deployed in biomedical devices and
assistive robots for improving the quality of life of disabled and elderly people, among which …

Background and objectives Automatic electrocardiogram (ECG) heartbeat classification is
substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of …

ECG signal occurs due to heart's electrical activity and helps detect and record people's
heart health. Many methods have been developed to classify ECG signal automatically. In …

Discriminative features have to be properly extracted and selected from the
electroencephalographic (EEG) signals of each specific subject in order to achieve an …

The developmental methods for evaluating the complexity of univariate signals has attracted
extensive attention. Therefore, entropy was discovered to be one of the best methods for …

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic
environments are becoming increasingly important in areas such as brain-computer …

Lung sound is one of the important information in the diagnosis of respiratory disease. Many
researchers have developed various algorithms to diagnose lung disease through the lung …

A lot of research has been carried out in the field of healthcare monitoring. In recent years,
development of patient monitoring system has been emerged as an area of research. In this …

Brain-computer interface (BCI) is a paradigm that offers an alternative communication
channel between neural activity generated in the brain and the user's external environment …