Epileptic Seizure Detection and Experimental Treatment: A Review

Abstract

One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the human head and body. In this article, we first discuss recent advances in seizure sensing, signal processing, time-or frequency-domain analysis, and classification algorithms to detect and classify seizure stages. Then, we show a strong potential of applying recent advancements in non-invasive brain stimulation technology to treat seizures. In particular, we explain the fundamentals of brain stimulation approaches, including (1) transcranial magnetic stimulation (TMS), (2) transcranial direct current stimulation (tDCS), (3) transcranial focused ultrasound stimulation (tFUS), and how to use them to treat seizures. Through this review, we intend to provide a broad view of both recent seizure diagnoses and treatments. Such knowledge would help fresh and experienced researchers to capture the advancements in sensing, detection, classification, and treatment seizures. Last but not least, we provide potential research directions that would attract seizure researchers/engineers in the field.

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control

“…Recently, artificial intelligence and machine learning techniques also including deep-learning methods have been applied to support the feature extraction and classification, Refs. [ 92 , 93 , 94 ]. Moreover, the success of advanced AI and deep-learning algorithms in epilepsy detection has opened the way to epilepsy prediction, where interictal signals that are observed between seizures are studied with the aim of extracting reliable markers of a future seizure [ 95 ].…”

Single gene polymorphisms as a predictor of noninvasive brain stimulation effectiveness (commentary on Pellegrini et al, 2021)

“…By using NIBS, we can investigate neuroplasticity, cerebral connectivity, and cortical excitability (Di Lazzaro et al, 2018; Reinhart et al, 2017). On the other side, we got a powerful new tool for the treatment of different neurophysiological disorders—from 2008 the Food and Drug Administration (FDA) in the United States certified five TMS devices for treatment of drug‐resistant major depressive disorder and protocols are being developed for the treatment of addiction (Yavari et al, 2016), chronic pain (Cardenas‐Rojas et al, 2020), stroke (Ovadia‐Caro et al, 2019), epilepsy (Kim et al, 2020), obsessive–compulsive disorder (Grover et al, 2021) and schizophrenia (Osoegawa et al, 2018). Special emphasis in these researches is placed on neurodegenerative diseases like Parkinson's disease (Madrid & Benninger, 2021) and Alzheimer's disease (Buss et al, 2019) considering that dementia is ranked as the seventh leading cause of death in the world with no known cure or effective way to stop the progression (World Health Organization, 2019).…”

Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection

“…Regardless of the measured biosignal(s), manual analysis of the output is a timeconsuming task. Automated seizure detection with machine learning has therefore re-ceived a lot of attention [10][11][12]. Impressive results have been obtained, though mostly on retrospective single-center datasets.…”