Since the manual detection of electrographic seizures in continuous electroencephalogram
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …

Around a third of the total population of Europe suffers from mental disorders. The use of
electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose …

Abstract Time-frequency (TF) analysis (TFA) is an effective tool to analyze time-varying
signals. The reassignment method (RM) and synchrosqueezing transform (SST) are high …

Epilepsy can now be diagnosed more accurately and quickly due to computer-aided seizure
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …

The artificial neural network (ANN) is the most popular research area in neural computing. A
multi-layer perceptron (MLP) is an ANN that has hidden layers. Feed-forward (FF) ANN is …

Most real-life signals exhibit non-stationary characteristics. Processing of such signals
separately in the time-domain or in the frequency-domain does not provide sufficient …

The instantaneous frequency (IF)-based post-processing methods, synchrosqueezing and
synchroextracting, can accurately characterize the time-varying frequency and amplitude of …

This paper provides a review of the Teager–Kaiser (TK) energy operator and its extensions
for signals and images processing. This class of operators possesses simplicity and good …

This paper proposes a new performance evaluation process for time-frequency distributions
(TFD) by designing a reference optimal TFD and novel accuracy and resolution measures …

Energy storage is recognized as a key technology for enabling the transition to a low-
carbon, sustainable future. Energy storage requires careful management, and capacity …