Seizure Detection Using the Phase-slope Index and Multichannel ECoG

Overview

Journal IEEE Trans Biomed Eng

Specialties Biomedical Engineering
Biophysics

Date 2012 Jan 25

PMID 22271828

Citations 10

Authors

Puneet Rana

John Lipor

Hyong Lee

Wim van Drongelen

Michael H Kohrman

Barry Van Veen

Affiliations

Soon will be listed here.

Abstract

Detection and analysis of epileptic seizures is of clinical and research interest. We propose a novel seizure detection and analysis scheme based on the phase-slope index (PSI) of directed influence applied to multichannel electrocorticogram data. The PSI metric identifies increases in the spatio-temporal interactions between channels that clearly distinguish seizure from interictal activity. We form a global metric of interaction between channels and compare this metric to a threshold to detect the presence of seizures. The threshold is chosen based on a moving average of recent activity to accommodate differences between patients and slow changes within each patient over time. We evaluate detection performance over a challenging population of five patients with different types of epilepsy using a total of 47 seizures in nearly 258 h of recorded data. Using a common threshold procedure, we show that our approach detects all of the seizures in four of the five patients with a false detection rate less than two per hour. A variation on the global metric is proposed to identify which channels are strong drivers of activity in each patient. These metrics are computationally efficient and suitable for real-time application.

Citing Articles

Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal.

Yedurkar D, Metkar S, Stephan T Cogn Neurodyn. 2024; 18(2):301-315.

PMID: 38699601 PMC: 11061070. DOI: 10.1007/s11571-021-09773-z.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Zhong X, Liu G, Dong X, Li C, Li H, Cui H Sensors (Basel). 2024; 24(1).

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Automated diagnosis of EEG abnormalities with different classification techniques.

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PMID: 37672143 DOI: 10.1007/s11517-023-02843-w.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.

Natu M, Bachute M, Gite S, Kotecha K, Vidyarthi A Comput Math Methods Med. 2022; 2022:7751263.

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Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury.

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