Nonstationary time series are ubiquitous in almost all natural and engineering systems.
Capturing the time-varying signatures from nonstationary time series is still a challenging …

The sleep spindles in EEG have become one type of biomarker used to assess cognitive
abilities and related disorders, and thus their detection is crucial for clinical research. This …

Data imbalance is a common problem in most practical classification applications of
machine learning, and it may lead to classification results that are biased towards the …

The primary objective of this research is to detect accounting fraud in Chinese family firms
through the utilization of imbalanced ensemble learning algorithms. It serves as the first …

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and
research. Early perception of the occurrence and development risk of cryptic transmission is …

Detection of sleep spindles, a special type of burst brainwaves recordable with
electroencephalography (EEG), is critical in examining sleep-related brain functions from …

Objective. Sleep spindles in the electroencephalogram (EEG) are significant in sleep
analysis related to cognitive functions and neurological diseases, and thus are of great …

Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML)
that is derived from quantitative electroencephalography (EEG) features has renewed …

Time-frequency representation (TFR) provides a good analysis for periodic signals;
however, they are insufficient for nonstationary signals. The synchrosqueezing transform …

As a hallmark of N2 sleep stage, sleep spindle detection based on electroencephalogram
(EEG) recordings plays a crucial role in analyzing sleep. Hence, how to effectively …