Detection of Epileptic Seizures Using Phase–Amplitude Coupling in Intracranial Electroencephalography

Abstract

Seizure detection using intracranial electroencephalography (iEEG) contributes to improved treatment of patients with refractory epilepsy. For that purpose, a feature of iEEG to characterize the ictal state with high specificity and sensitivity is necessary. We evaluated the use of phase–amplitude coupling (PAC) of iEEG signals over a period of 24 h to detect the ictal and interictal states. PAC was estimated by using a synchronisation index (SI) for iEEG signals from seven patients with refractory temporal lobe epilepsy. iEEG signals of the ictal state was characterised by a strong PAC between the phase of β and the amplitude of high γ. Furthermore, using SI values, the ictal state was successfully detected with significantly higher accuracy than by using the amplitude of high γ alone. In conclusion, PAC accurately distinguished the ictal state from the interictal state.

Persistent aberrant cortical phase–amplitude coupling following seizure treatment in absence epilepsy models

“…; Edakawa et al . ), but has not yet been evaluated in generalized epilepsies. Critically, PAC is a potentially useful metric as the correlation between different oscillatory EEG components may vary without overt changes in the power spectrum, and can therefore uniquely differ for similar power spectra.…”

“…One common type of cross-frequency coupling is phase-amplitude coupling (PAC) where the phase of a slower oscillation is correlated with the amplitude of a faster oscillation. The magnitude of PAC represents the putative interactions between underlying circuits which behave abnormally in models of focal epilepsy (Guirgis et al 2015;Amiri et al 2016;Edakawa et al 2016), but has not yet been evaluated in generalized epilepsies. Critically, PAC is a potentially useful metric as the correlation between different oscillatory EEG components may vary without overt changes in the power spectrum, and can therefore uniquely differ for similar power spectra.…”

Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features

“…CFC can be involved in normal brain processes such as memory encoding [36] or auditory processing [37]. However, it can also be involved in pathology: in epilepsy, CFC has been tied to seizure states and epileptogenic tissue [21] - [25]. The global averaging across channel locations of the scalp CFC map, and the subsequent thresholding by th cf c , appear to be an appropriate selection process for identifying seizure state transitions in scalp EEG.…”

“…Delta-HFO CFC was also shown to discriminate between epileptogenic and non-epileptogenic tissues in the brain recorded during nonseizure periods when patients were experiencing non-REM sleep [23]. Delta-HFO CFC measures have also been shown to identify seizure states, as well as the transitions between them using unsupervised machine learning [24] and have been used as features to detect seizures in long term iEEG recordings [25]. The ability of CFC to discriminate epileptic activity both spatially and temporally in the iEEG suggests that it may also be a viable feature for detecting seizure state transitions leading up to seizure in the scalp EEG.…”

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

“…The result showed in the above section that the selection of prominent features and the oversampling method can significantly improve the performance of an automatic system. Hence, we are the first one to use high-frequency components (ripple and fast ripple) from interictal iEEG, the performances of localizing individual segments were observed in terms of sensitivity, specificity, precision, fallout, and F-score for the comparison study similar to HFOs-and low frequency-based related studies 10,[19][20][21][31][32][33][34][35] computed from the confusion matrix (shown in Table 6). It is observed from the Table 2 that the proposed method achieved the highest performance for localizing individual segments with the adult patients Pt5 (SEN: 79.25%; fall-out: 2.50%), Pt6 (SEN: 54.82%; fall-out: 3.58%), and Pt8 (SEN: 88.52%; fall-out: 1.46%).…”