Objective Prediction of epileptic seizures can improve the living conditions for refractory
epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and …

With the increase in biosensors and data collection devices in the healthcare industry,
artificial intelligence and machine learning have attracted much attention in recent years. In …

In this paper, we present a parallel framework based on MPI for a large dataset to extract
power spectrum features of EEG signals so as to improve the speed of brain signal …

Sleep apnea (SA) is a ubiquitous sleep‐related respiratory disease. It can occur hundreds of
times at night, and its long‐term occurrences can lead to some serious cardiovascular and …

Although the frequency band segmentation rules used in fast kurtogram (FK) has made
great achievements in locating fault resonance band, it still has the problem of …

Depression, a common mental illness worldwide, needs to be diagnosed and cured at an
early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is …

Background As the human behavior is influenced by both cognition and emotion, affective
computing plays a central role in human-machine interaction. Algorithms for emotions …

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a
long-standing objective for system manufacturers and researchers in the additive …

Power Spectral Density (PSD) is an essential representation of the signal spectrum that
depicts the power measurement content versus frequency. PSD is typically used to …

This article proposes the design of an automatic classifier using the empirical mode
decomposition (EMD) along with machine learning techniques for identifying the five most …