This paper presents a novel hybrid model for fault detection and classification of motor
bearing. In the proposed model, permutation entropy (PE) of the vibration signal is …

Mild cognitive impairment (MCI) can be an indicator representing the early stage of
Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major …

The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which
has affected million people in the world. Hence, an early detection of the focal epileptic …

In the signal analysis context, the entropy concept can characterize signal properties for
detecting anomalies or non-representative behaviors in fiscal systems. In motor fault …

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant
importance. Combining adaptive optimal kernel time-frequency representation and visibility …

Diversity in brain health is influenced by individual differences in demographics and
cognition. However, most studies on brain health and diseases have typically controlled for …

With the frequent occurrence of irregular events in recent years, the tourism industry in some
areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy …

This paper proposes a fast weighted horizontal visibility graph constructing algorithm
(FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated …

Abstract Dispersion Lempel–Ziv complexity (DLZC) and multiscale DLZC (MDLZC) are very
recently introduced complexity indicators to quantify the dynamic change of time series in …

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time
series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is …