Non-EEG seizure-detection systems and potential SUDEP prevention: State of the art

Vel, Anouk Van de; Cuppens, Kris; Bonroy, Bert; Milosevic, Milica; Jansen, Katrien; Huffel, Sabine Van; Vanrumste, Bart; Lagae, Lieven; Ceulemans, Berten

doi:10.1016/j.seizure.2013.02.012

Cited by 95 publications

(55 citation statements)

References 66 publications

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“…The literature was searched in PubMed using the terms “seizure detection,” “seizure device,” and “seizure monitoring.” Abstracts were screened, and only papers on fully automated monitoring were selected, excluding reports on EEG‐based systems, systems using only one modality, and those concerning neonates and very young infants. Finally, 5 recent reviews on seizure detection devices were also reviewed for missed publications. Only full papers were considered.…”

Section: Methodsmentioning

confidence: 99%

Multimodal seizure detection: A review

Leijten

2018

Epilepsia

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Summary A review is given on the combined use of multiple modalities in non electroencephalography (EEG)‐based detection of motor seizures in children and adults. A literature search of papers was done on multimodal seizure detection with extraction of data on type of modalities, study design and algorithm, sensitivity, false detection rate, and seizure types. Evidence of superiority was sought for using multiple instead of single modalities. Seven papers were found from 2010 to 2017, mostly using contact sensors such as accelerometers (n = 5), electromyography (n = 2), heart rate (n = 2), electrodermal activity (n = 1), and oximetry (n = 1). Remote sensors included video, radar, movement, and sound. All studies but one were in‐hospital, with video‐EEG as a gold standard. Algorithms were based on physiology and supervised machine learning, but did not always include a separate test dataset. Sensitivity ranged from 4% to 100% and false detection rate from 0.25 to 20 per 8 hours. Tonic–clonic seizure detection performed best. False detections tended to be restricted to a minority (16%‐30%) of patients. Use of multiple sensors increased sensitivity; false detections decreased in one study, but increased in another. These preliminary studies suggest that detection of tonic–clonic seizures might be feasible, but larger field studies are required under more rigorous design that precludes bias. Generic algorithms probably suffice for the majority of patients.

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Section: Methodsmentioning

confidence: 99%

Multimodal seizure detection: A review

Leijten

2018

Epilepsia

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“…The goal is to provide a set of noninvasive, nonintrusive, reasonable-cost sensors that allow the patient to run a normal life while providing reliable warnings in real time. Here, we focus on monitoring, predicting, and warning epileptic patients from potentially dangerous seizures [38]. The gold standard in monitoring epileptic seizures is combining EEG and video, where EEG is manually analyzed by experts and the whole diagnostic procedure requires a stay of up to several days in a hospital setting.…”

Section: B Biomedical Healthmentioning

confidence: 99%

“…Therefore, a primary challenge is to understand the link between epileptic seizures and additional body parameters: movement, breathing, heart rate, and others. Due to the fact that epileptic seizures vary within and across patients, and due to the complex relations between different body systems, it is likely that any such system should rely on more than one modality [38].…”

Section: B Biomedical Healthmentioning

confidence: 99%

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

2015

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This paper provides an overview of the main challenges in multimodal data fusion across various disciplines and addresses two key issues: ''why we need data fusion'' and ''how we perform it.'' ABSTRACT | In various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term ''modality'' for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or ''challenges,'' are common to multiple domains. This paper deals with two key issues: ''why we need data fusion'' and ''how we perform it.''The first issue is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second issue, ''diversity'' is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the data sets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects, and the opportunities that it holds.

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“…18,28,29 To evaluate semiology, movements can be analyzed using accelerometry, surface electromyography, video detection systems, mattress sensor, and seizure-alert dogs; physiological signals can be evaluated by electrocardiography, skin temperature, audio classification, and respiratory rate changes techniques. The evaluation of the manifestation of epilepsy is vital to defining the accurate monitoring and treatment procedure.…”

Section: Traditional Approaches To Automatic Epilepsy Evaluationmentioning

confidence: 99%

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

et al. 2017

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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.

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Non-EEG seizure-detection systems and potential SUDEP prevention: State of the art

Abstract: The most effective seizure detection systems are multimodal. Such systems should also be comfortable and low-power. The body signals and modalities on which a system is based should take account of the user's seizure types and personal preferences.

Cited by 95 publications

References 66 publications

Multimodal seizure detection: A review

Multimodal seizure detection: A review

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

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

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