Epilepsy is a chronic neurological condition that affects approximately 70 million people
worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as …

This paper presents a novel systematic approach for patient-specific classification of long-
term Electroencephalography (EEG). The goal is to extract the seizure sections with a high …

Objective. Significant progress has been witnessed in within-subject seizure detection from
electroencephalography (EEG) signals. Consequently, more and more works have been …

Bipolar disorder (BD) is a severe psychiatric disorder and has two common types: type I and
type II. Early diagnosis of the subtypes is very challenging particularly in adolescence. In this …

Epileptic seizure detection classification distinguishes between epileptic and non-epileptic
signals and is an important step that can aid doctors in diagnosing and treating epileptic …

Neural networks are seeing increased use in diverse Internet of Things (IoT) applications
such as healthcare, smart homes and industrial monitoring. Their widespread use makes …

The contemporary diagnosis of epileptic seizures is dominated by non-invasive EEG signal
analysis and classification. In this paper, we propose a patient-specific seizure detection …

Modelling 3D objects in CAD software requires special skills which require a novice user to
undergo a series of training exercises to obtain. To minimize the training time for a novice …

Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist
during the diagnosis of epilepsy. During last few years lot of work has been done in the field …

This paper proposes a strategy for improving the correct diagnosis of epilepsy based on
electroencephalogram (EEG) using a spatio-temporal variable structure graph convolutional …