This paper presents a tutorial review of recent advances in the field of time–frequency (t, f)
signal processing with focus on exploiting (t, f) image feature information using pattern …

Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once
compressively acquired, many signals need to be processed using advanced techniques …

Background and objective Surface electromyography (sEMG) based muscle fatigue
research is widely preferred in sports science and occupational/rehabilitation studies due to …

Accurate recognition and understating of human emotions is an essential skill that can
improve the collaboration between humans and machines. In this vein …

Time–frequency representation (TFR) is useful for non‐stationary signal analysis as it
provides information about the time‐varying frequency components. This study proposes a …

Time-frequency (TF) based machine learning methodologies can improve the design of
classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF …

Objectives Hypoxic ischaemic encephalopathy is a significant cause of mortality and
morbidity in the term infant. Electroencephalography (EEG) is a useful tool in the …

Background Cardiotocography (CTG) signal abnormality classification plays an important
role in the diagnosis of abnormal fetuses. This classification problem is made difficult by the …

This paper considers the general problem of detecting change in non-stationary signals
using features observed in the time–frequency (t, f) domain, obtained using a class of …

Objective. To develop an automated system to classify the severity of hypoxic-ischaemic
encephalopathy injury (HIE) in neonates from the background electroencephalogram (EEG) …