Detection of epileptic seizures in scalp electroencephalogram

an automated real-time wavelet-based approach.

Ali Shahidi Zandi, Guy A. Dumont, Manouchehr Javidan, Reza Tafreshi

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

This study evaluates a new automated patient-specific method for epileptic seizure detection using scalp electroencephalogram (EEG). The method relies on a normalized wavelet-based index, named the combined seizure index (CSI), and requires a seizure example and a nonseizure EEG interval as reference. The CSI is derived for every epoch in each EEG channel and is sensitive to both the rhythmicity and relative energy of that epoch and the consistency of EEG patterns among different channels. Increasing significantly as seizures occur, the CSI is monitored using a one-sided cumulative sum test to generate appropriate alarms in each channel. A seizure alarm is finally generated according to channel-based information. The proposed method was evaluated using the scalp EEG test data of approximately 236 hours from 26 patients with a total of 79 focal seizures, achieving a high sensitivity of approximately 91% with a false detection rate of 0.33 per hour and a median detection latency of 7 seconds. In addition, statistical analysis revealed that the average CSI around the onset on the side of the focus in patients with temporal lobe epilepsy (TLE) is significantly greater than that of the opposite side (P < 0.001), indicating the capability of this index in lateralizing the seizure focus in this type of epilepsy.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalJournal of clinical neurophysiology : official publication of the American Electroencephalographic Society
Volume29
Issue number1
Publication statusPublished - Feb 2012

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Scalp
Electroencephalography
Epilepsy
Seizures
Temporal Lobe Epilepsy
Periodicity

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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title = "Detection of epileptic seizures in scalp electroencephalogram: an automated real-time wavelet-based approach.",
abstract = "This study evaluates a new automated patient-specific method for epileptic seizure detection using scalp electroencephalogram (EEG). The method relies on a normalized wavelet-based index, named the combined seizure index (CSI), and requires a seizure example and a nonseizure EEG interval as reference. The CSI is derived for every epoch in each EEG channel and is sensitive to both the rhythmicity and relative energy of that epoch and the consistency of EEG patterns among different channels. Increasing significantly as seizures occur, the CSI is monitored using a one-sided cumulative sum test to generate appropriate alarms in each channel. A seizure alarm is finally generated according to channel-based information. The proposed method was evaluated using the scalp EEG test data of approximately 236 hours from 26 patients with a total of 79 focal seizures, achieving a high sensitivity of approximately 91{\%} with a false detection rate of 0.33 per hour and a median detection latency of 7 seconds. In addition, statistical analysis revealed that the average CSI around the onset on the side of the focus in patients with temporal lobe epilepsy (TLE) is significantly greater than that of the opposite side (P < 0.001), indicating the capability of this index in lateralizing the seizure focus in this type of epilepsy.",
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