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Med Biol Eng Comput. 2017 Sep;55(9):1659-1668. doi: 10.1007/s11517-017-1616-z. Epub 2017 Feb 9.

Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia.

Author information

1
Biomedical Engineering Department, Rochester Institute of Technology, Rochester, NY, USA.
2
Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
3
Departments of Pediatrics and Biomedical Genetics, Center for Neural Development and Disease, University of Rochester Medical Center, Rochester, NY, USA.
4
Department of Computer and Electrical Engineering, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL, 33431, USA. bghoraani@fau.edu.

Abstract

Infantile spasms (ISS) is a devastating epileptic syndrome that affects children under the age of 1 year. The diagnosis of ISS is based on the semiology of the seizure and the electroencephalogram (EEG) background characterized by hypsarrhythmia (HYPS). However, even skilled electrophysiologists may interpret the EEG of children with ISS differently, and commercial software or existing epilepsy detection algorithms are not helpful. Since EEG is a key factor in the diagnosis of ISS, misinterpretation could result in serious consequences including inappropriate treatment. In this paper, we developed a novel algorithm to localize the relevant electrical abnormality known as epileptic discharges (or spikes) to provide a quantitative assessment of ISS in HYPS. The proposed algorithm extracts novel time-frequency features from the EEG signals and localizes the epileptic discharges associated with ISS in HYPS using a support vector machine classifier. We evaluated the proposed method on an EEG dataset with ISS subjects and obtained an average true positive and false negative of 98 and 7%, respectively, which was a significant improvement compared to the results obtained using the clinically available software. The proposed automated method provides a quantitative assessment of ISS in HYPS, which could significantly enhance our knowledge in therapy management of ISS.

KEYWORDS:

Classification; Feature extraction; Hypsarrythmia; Nonnegative matrix factorization; Time–frequency representations

PMID:
28185049
DOI:
10.1007/s11517-017-1616-z
[Indexed for MEDLINE]

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