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1.
Epilepsy Behav ; 26(1): 81-6, 2013 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-23247268

RESUMO

Retrospective data analysis was performed in a sample of 45 consecutive patients who underwent epilepsy surgery for medically refractory mTLE-HS. Beck Depression Inventory (BDI) was used preoperatively to detect actual depressive symptoms and label patients into those "with depressive symptoms" or "without depressive symptoms". Postoperative seizure outcome one, two, and three years after surgery was classified into "complete seizure freedom" versus "presence of auras and/or seizures". Postoperative seizure outcomes were compared in patients with and without depressive symptoms, and no significant difference of postoperative seizure outcome was found. However, there was a non-significant trend for patients with preoperative depressive symptoms to experience a postoperative running down phenomenon more frequently than nondepressed patients. Depressive symptoms, identified by the BDI, do not seem to have a predictive value for postoperative seizure outcome in this highly selected patient population with mTLE-HS, but may be positive predictors for experiencing a postoperative running down phenomenon.


Assuntos
Lobectomia Temporal Anterior/efeitos adversos , Depressão/etiologia , Epilepsia do Lobo Temporal/complicações , Hipocampo/patologia , Complicações Pós-Operatórias/fisiopatologia , Convulsões/diagnóstico , Adulto , Distribuição de Qui-Quadrado , Eletroencefalografia , Epilepsia do Lobo Temporal/cirurgia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Complicações Pós-Operatórias/diagnóstico , Valor Preditivo dos Testes , Escalas de Graduação Psiquiátrica , Esclerose/complicações , Convulsões/etiologia , Resultado do Tratamento
2.
Artigo em Inglês | MEDLINE | ID: mdl-22255730

RESUMO

An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.


Assuntos
Algoritmos , Diagnóstico por Computador/métodos , Eletroencefalografia/métodos , Oscilometria/métodos , Convulsões/diagnóstico , Software , Humanos , Sistemas On-Line , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
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