The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-
term EEG recordings of an epileptic patient contain a huge amount of EEG data. Therefore …

Epilepsy is a common neurological disorder affecting both children and adults. It can trigger
seizures without any stimuli. An accurate diagnosis of epilepsy is essential to the treatment …

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and
a conventional classifier for epileptic seizure detection. The signal segments of a channel of …

Due to the rapid development of artificial intelligence, seizure detection has achieved great
success in terms of accuracy and speed. However, low-power seizure detection algorithms …

The efficient compression and classification of medical signals, particularly
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …

Automatic detection of spike-and-wave discharges (SWDs) of absence seizures, is a highly
time-consuming process requiring trained technicians or neurologists to categorize …

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will
play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder …

One of the challenges in monitoring machinery vibration is handling the huge amount of
data that must be transmitted, stored, and analyzed before transforming it into useful …

The efficient compression and classification of medical signals, particularly
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …

In this paper, we utilized a systematic literature review scheme to understand the current
methods utilized to compress multichannel Electroencephalography (EEG) signals and how …