Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

Abstract

Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

Rhythmic rocking stereotypies in frontal lobe seizures: A quantified video study

“…Oro-alimentary automatisms may also occur rhythmically [3,31]. Amongst possible methods of movement quantification in neurological disorders (for comprehensive review, see [12]), there is increasing interest in video analysis techniques, including those based on deep learning or machine learning, for automated analysis of movements in epileptic seizures [1,2,7,8,33] and for motor stereotypies (e.g., in autism [20]). However, such studies have tended to focus on detection and categorization of movement patterns; quantification of multi-segmental rhythmic behaviors in terms of time-evolving movement frequencies has not yet been reported.…”

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images

“…A number of EEG signal features have been considered to represent seizures (Ahmedt-Aristizabal et al, 2017; Baldassano et al, 2017; Orosco et al, 2013; Venkataraman et al, 2014); e.g., time-frequency analysis (Anusha et al, 2012; Gao et al, 2017; Li et al, 2018), wavelet transform (Adeli et al, 2003, Adeli et al, 2007; Adeli and Ghosh-Dastidar, 2010; Ayoubian et al, 2013; Faust et al, 2015; Sharma et al, 2014; Yuan et al, 2018), and nonlinear analysis (Ghosh-Dastidar et al, 2007; Takahashi et al, 2012). The detection accuracy has also improved with advances in machine learning algorithms such as the support vector machine (Satapathy et al, 2016), logistic regression (Lam et al, 2016), and neural networks (Adeli and Ghosh-Dastidar, 2010; Ghosh-Dastidar et al, 2008; Ghosh-Dastidar and Adeli, 2007, Ghosh-Dastidar and Adeli, 2009; Juárez-Guerra et al, 2015).…”

“…A number of recent studies demonstrated the efficacy of deep learning in the classification of EEG signals (AliMardani et al, 2016; Dvey-Aharon et al, 2017; Jirayucharoensak et al, 2014; Ma et al, 2015; Schirrmeister et al, 2017). Yet, the performance of seizure detection by deep learning still requires improvement (Acharya et al, 2018; Ahmedt-Aristizabal et al, 2017, 2018; Thodoroff et al, 2016) compared to the level of human performance in certain visual recognition tasks (Dodge and Karam, 2017; Esteva et al, 2017).…”

Aberrant epileptic seizure identification: A computer vision perspective

“…However, the study of these signs relies heavily on clinical experience and training. Given the importance of body motion patterns in the assessment of epilepsy, prior works have demonstrated that automated analysis of semiological patterns based on computer vision can support diagnosis by standard and objective assessment methods among evaluators [3,4]. Previously, we proposed a facial semiology analysis approach to identify patients with mesial temporal lobe epilepsy (MTLE) [5], and a hierarchical multi-modal system to quantify and classify patients with MTLE and extra-temporal lobe epilepsy (ETLE) based on face, body and hand motions [6].…”