Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures

Abstract

Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (eeG). As a noninvasive monitoring method to record brain electrical activities, eeG has been widely used for capturing the underlying dynamics of disruptive neuronal responses across the brain in real-time to provide clinical guidance in support of epileptic seizure treatments in practice. In this study, we introduce a novel dynamic learning method that first infers a time-varying network constituted by multivariate eeG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory. We demonstrate the efficacy of our learning method to detect relatively strong synchronization (characterized by the algebraic connectivity metric) caused by abnormal neuronal firing during a seizure onset. The computational results for a realistic scalp EEG database show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore, our method observes potential pre-seizure phenomena in some cases. The human brain is undoubtedly one of the most complex dynamic systems known to mankind, which consists of billions of interconnected neurons 1. The connectivity patterns across different brain regions could convey evidences for advancing and supporting the understanding of cognitive and behavioral functions 2-5. In the past decades, many research studies have been devoted to discovering the process and mechanisms that govern the communication between neurons; in particular, how and why neuronal networks are formed for various neurological disorders. Epilepsy is one of the most common central nervous system disorders, following Alzheimer's disease and stroke. According to recent statistics 6 , nearly 4% of people across different ages are diagnosed with epilepsy and suffer from epileptic seizure occurrence and recurrence during their lifetimes. Epileptic seizures develop with a sudden abnormal surge of electrical activities of pathological, synchronous neuronal firing in all brain regions or some parts. Seizures can be controlled with medications and/or invasive surgeries in 70% of diagnosed patients 7. However, prompt detection or even prediction of seizure onsets remains a critical and challenging task to date because of its high variation patterns of occurrence and manifestation from one individual to another. To this end, a lot of efforts have been given to developing subject-specific, data-driven methodologies aiming at ultimately improving individualized seizure onset detection and prediction 8. Electroencephalography (EEG) recordings in high temporal resolution, regardless of arguable evidence of poor spatial resolution and low signal-to-noise ratio (SNR), are useful amongst neuroimaging techniques allowing clinicians to collect and monitor global neuronal activity information in the brain of epileptic patients. Epilepti...

Spatiotemporal dynamics of maximal and minimal EEG spectral power

“…Third, EEG temporal derivatives are drift free. Prior studies used EEG temporal derivatives for similar reasons [e.g., [43][44][45], providing some evidence suggesting that EEG temporal derivatives yield more effective neural features than EEG for braincomputer interface [45].…”

A survey of brain network analysis by electroencephalographic signals

“…Especially, in medical and neurological projects, AI is helping researchers and doctors to explore the complex brain. Shreds of evidence verify that AI algorithms can estimate reliable causal relationships among multi-layer neural perceptrons in memory recognition tasks with considered time-lag and different initial conditions (Talebi et al 2018), and detect strong synchronization and potential pre-seizure Cognitive Neurodynamics phenomena (Bomela et al 2020). Massive amounts of papers on signal processing propose types of AI algorithms to solve EEG signal processing and network construction.…”

“…Compared with structural equation modeling for static dependencies of brain regions (Friston 2011), dynamical causal modeling (DCM) based on Bayesian framework was first proposed to analyze nonlinear brain network connectivity with a deterministic causal model of neuronal responses to external perturbation (Friston et al 2003) for first fMRI data then EEG (David et al 2006;Fastenrath et al 2009), and reveals how neural activity is generated and neuronal variables fluctuate over separable timescales (Cooray et al 2016). DCM can provide more promising and helpful psychological and neuropsychological signatures, such as on schizophrenia (Fogelson et al 2014;Friston et al 2016a;Zhou et al 2018), Alzheimer's disease (Penny et al 2018), epileptic seizures (Bomela et al 2020;Cooray et al 2016), stroke (Bo ¨nstrup et al 2016(Bo ¨nstrup et al , 2018, drug-abstinent (Zhao et al 2017), psychotic disorders (Dı ´ez et al 2017) and so on.…”

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

“…Then, a filter based on Fourier transform was applied to divide the EEG into five bands. These five bands-gamma (30-60 Hz), beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), alpha (8-15 Hz), theta (4-8 Hz) and delta (0.5-4 Hz)-and unfiltered raw EEG were used for subsequent calculations.…”