Data mining is the process of extraction useful patterns and models from a huge dataset.
These models and patterns have an effective role in a decision making task. Data mining …

One of the most powerful tools to assess the dynamical characteristics of time series is
entropy. Sample entropy (SE), though powerful, is not fast enough, especially for long …

The use of system logs for detecting and troubleshooting anomalies of production systems
has been known since the early days of computers. In spite of the advances in the area, the …

Permutation entropy (PE) is a well-known and fast method extensively used in many
physiological signal processing applications to measure the irregularity of time series …

Biomedical research is drowning in data, yet starving for knowledge. Current challenges in
biomedical research and clinical practice include information overload–the need to combine …

Background and objective Signal segmentation and spike detection are two important
biomedical signal processing applications. Often, non-stationary signals must be segmented …

A variety of problems in, eg, discrete mathematics, computer science, information theory,
statistics, chemistry, biology, etc., deal with inferring and characterizing relational structures …

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 …

Stroke survivors are more prone to developing cognitive impairment and dementia.
Dementia detection is a challenge for supporting personalized healthcare. This study …

Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series
over multiple temporal scales. Recent developments in the field have tried to extend the …