Comput Math Methods Med - Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.

Tópicos

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Resumo

The aim of this paper is to develop an automated system for epileptic seizure prediction from intracranial EEG signals based on Hilbert-Huang transform (HHT) and Bayesian classifiers. Proposed system includes decomposition of the signals into intrinsic mode functions for obtaining features and use of Bayesian networks with correlation based feature selection for binary classification of preictal and interictal recordings. The system was trained and tested on Freiburg EEG database. 58 hours of preictal data, 40-minute data blocks prior to each of 87 seizures collected from 21 patients, and 503.1 hours of interictal data were examined resulting in 96.55% sensitivity with 0.21 false alarms per hour, 13.896% average proportion of time spent in warning, and 33.21 minutes of average detection latency using 30-second EEG segments with 50% overlap and a simple postprocessing technique resulting in a decision (a seizure is expected/not expected) every 5 minutes. High sensitivity and low false positive rate with reasonable detection latency show that HHT based features are acceptable for patient specific seizure prediction from intracranial EEG data. Time spent for testing an EEG segment was 4.1451 seconds on average, which makes the system viable for use in real-time seizure control systems.

Resumo Limpo

aim paper develop autom system epilept seizur predict intracrani eeg signal base hilberthuang transform hht bayesian classifi propos system includ decomposit signal intrins mode function obtain featur use bayesian network correl base featur select binari classif preictal interict record system train test freiburg eeg databas hour preictal data minut data block prior seizur collect patient hour interict data examin result sensit fals alarm per hour averag proport time spent warn minut averag detect latenc use second eeg segment overlap simpl postprocess techniqu result decis seizur expectednot expect everi minut high sensit low fals posit rate reason detect latenc show hht base featur accept patient specif seizur predict intracrani eeg data time spent test eeg segment second averag make system viabl use realtim seizur control system

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