Seizure Prediction and Detection via Phase and Amplitude Lock Values

Abstract

A robust seizure prediction methodology would enable a “closed-loop” system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998; Ben-Menachem, 2001), while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV) which calculate the difference of phase and amplitude between electroencephalogram (EEG) electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the “CHB-MIT Scalp EEG Database” which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

Volumetric measurement of synchronization of the dust acoustic wave with an external modulation

“…[7] The adjustment of the selfexcited dust acoustic wave mode to match the externally applied modulation is a nonlinear process known as (phase) synchronization; a process that is ubiquitous in a variety of physical systems and is believed to play an important role in many self-organized phenomena. [8][9][10][11][12][13] In this paper, we present a measurement showing the volumetric nature of this synchronization process by applying a time-resolved Hilbert Transform [14,15] to high-speed imaging of the dust acoustic wave in an rf glow discharge plasma.…”

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

“…In most cases, noises and interferences are closely related to the patients, which needs expensive manual processing even if this is currently only feasible in theory. 13 Basically, there exists no general solution to reliable EEG denoising. • In the context of conventional EEG classification, denoising of high quality is a prerequisite for feature extraction.…”

“…However, sufficient a priori knowledge is often required to effectively separate noises and interferences from the weak signal, without which, the original intrinsic information of EEG could be lost or destroyed. In most cases, noises and interferences are closely related to the patients, which needs expensive manual processing even if this is currently only feasible in theory . Basically, there exists no general solution to reliable EEG denoising. In the context of conventional EEG classification, denoising of high quality is a prerequisite for feature extraction .…”

Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals

“…Similar intervals have been considered in [ 38 , 51 , 52 ]. Based on literature, it has been reported that there are electrophysiological changes, which might develop minutes to hours before the actual seizure onset [ 38 , 47 , 54 ]. Therefore, the preictal training data could be selected from any of the following options: Preictal-0: the preictal training interval ends right at the beginning of seizure onset.…”

“…Note that the data segment preceding the seizure onset is called the preictal interval and ranges from a few seconds to several hours long [ 38 , 47 , 54 ]. The performance of the proposed predictor is compared with the random and Poisson predictors and with existing sEEG-based prediction methods [ 17 , 18 , 28 , 41 , 45 , 47 , 48 , 54 , 55 ]. The results show that the proposed prediction method could be of potential value for early warnings for epileptic patients and/or their caregivers.…”