Temporal epilepsy seizures monitoring and prediction using cross-correlation and chaos theory

Healthcare Technology Letters

article

Tahar Haddad¹  Naim Ben-Hamida²  Larbi Talbi¹  Ahmed Lakhssassi¹  Sadok Aouini² 

[1] Département d'informatique et d'ingénierie, Université du Québec en Outaouais;Ciena Canada

关键词: electroencephalography;    neurophysiology;    patient monitoring;    medical disorders;    chaos;    biomedical electrodes;    entropy;    statistical analysis;    medical signal processing;    time 200 h;    frequency 60 Hz to 120 Hz;    false-positive rate;    Freiburg database;    seizure signature;    threshold levels;    statistical analysis;    power spectral density;    entropy;    Lyapunov index;    electrodes;    gamma subbands;    beta subbands;    alpha subbands;    theta subbands;    delta subbands;    electroencephalography signals;    cross-correlation theory;    epileptic patients;    hippocampal area;    chaos theory;    temporal epilepsy seizure prediction;    temporal epilepsy seizure monitoring;   

DOI  :  10.1049/htl.2013.0010

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Temporal seizures due to hippocampal origins are very common among epileptic patients. Presented is a novel seizure prediction approach employing correlation and chaos theories. The early identification of seizure signature allows for various preventive measures to be undertaken. Electro-encephalography signals are spectrally broken down into the following sub-bands: delta; theta; alpha; beta; and gamma. The proposed approach consists of observing a high correlation level between any pair of electrodes for the lower frequencies and a decrease in the Lyapunov index (chaos or entropy) for the higher frequencies. Power spectral density and statistical analysis tools were used to determine threshold levels for the lower frequencies. After studying all five sub-bands, the analysis has revealed that the seizure signature can be extracted from the delta band and the high frequencies. High frequencies are defined as both the gamma band and the ripples occurring within the 60–120 Hz sub-band. To validate the proposed approach, six patients from both sexes and various age groups with temporal epilepsies originating from the hippocampal area were studied using the Freiburg database. An average seizure prediction of 30 min, an anticipation accuracy of 72%, and a false-positive rate of 0% were accomplished throughout 200 h of recording time.

【 授权许可】

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