Epileptic seizure detection on patients with mental retardation based on EEG features: A pilot study

Abstract

Mental retardation (MR) is one of the most common secondary disabilities in people with Epilepsy. However, to our knowledge there are no reliable seizure detection methods specified for MR-patients. In this paper we performed a pilot study on a group of six patients with mental retardation to assess what EEG features potentially work well on this group. A group of EEG features on the time, frequency and spatio-temporal domain were extracted, the modified wrapper approach was then employed as an improved feature subset selection method. Results show high variance on obtained features subset across this group, meanwhile there exist some common features which characterize the high-frequency components of epileptic EEG signals.

Seizure pattern-specific epileptic epoch detection in patients with intellectual disability

“…In clinical practice, we encounter differences on the following aspects: (1) abnormal background EEG (slow activity, no alpha), (2) abnormal sleep/wake cycles (difficult to interpret sleep/drowsiness EEG), (3) frequent occurrence of focal anomalies, (4) high levels of inter-ictal epileptic transients that resemble seizure activity, and (5) different seizure discharge patterns (for example predominant fast spikes in tonic seizures). Indeed, previous studies [8,11] showed that EEG-based seizure detection was difficult for this population. It is therefore necessary to evaluate the state-of-the-art EEG features for this specific population.…”

“…The wrapper-based sequential forward selection (SFS) [11] was performed in this study. In the wrapper approach, a classifier (QDA in this study) was constructed with different candidate feature subsets.…”

EEG-based seizure detection in patients with intellectual disability: Which EEG and clinical factors are important?

“…Therefore, we did not perform a random patient selection from the ID population. Based on the seizure detection performance in our pilot study [22], we needed at least eight patients for each EEG discharge pattern (˛ = 0.05, ˇ = 0.2).…”

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures

“…The seizure detection (Wang et al, 2015(Wang et al, , 2016(Wang et al, , 2017 for a specific population with both epilepsy and intellectual disability (ID) is challenging based on existing EEG features due to the presence of abnormal EEG activities caused by cerebral development disorders (Steffenburg et al, 1998;Guerrini et al, 2001). Clinicians often encountered different types of EEG in ID patients, such as abnormal background EEG (slow activity, no alpha), frequent occurrence of focal anomalies, high levels of inter-ictal epileptic transients that resemble seizures, abnormal sleep and wake cycles (difficult to interpret sleep/drowsiness EEG), as well as different seizure discharge patterns from non-ID epileptic patients.…”