Български
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Computers in Biology and Medicine 2020-Sep

Classification of the interictal state with hypsarrhythmia from Zika Virus Congenital Syndrome and of the ictal state from epilepsy in childhood without hypsarrhythmia in EEGs using entropy measures

Само регистрирани потребители могат да превеждат статии
Вход / Регистрация
Линкът е запазен в клипборда
Priscila Rocha
Allan Barros
Washington Silva
Gean Sousa
Patrícia Sousa
Antônio da Silva

Ключови думи

Резюме

This paper intends to classify the interictal state with hypsarrhythmia in patients with Zika Virus Congenital Syndrome (ZVCS) and of the ictal state in patients with epilepsy in childhood without the presence of hypsarrhythmia. Hypsarrhythmia is a specific interictal chaotic morphology, and the correct distinction between these two EEG states is crucial to improving the cognitive development of these epileptic patients. The proposed approach was assessed using the proprietary database of Casa Ninar, which contains data regarding children from northeastern Brazil born with microcephaly caused by the Zika virus. We also used data from the CHB-MIT database. Fundamental rhythms of the EEG signal δ, θ, α, and β were analyzed, and then decomposed by Discrete Wavelet Transform, in which 45 mother wavelet functions were tested to determine the most appropriate function to represent the EEG signals in the hypsarrhythmia interictal and ictal states. We extracted Shannon, Log Energy, Norm, and Sure entropy measures of the subbands as relevant features, and the combinations among them were applied in the state-of-the-art machine learning methods. The combination of Sure entropy with Shannon entropy, or with Log Energy and Norm, extracted from the δ rhythm, allowed for the best linear separability between the classes in most of the classifiers, obtaining 100% accuracy, sensitivity, and specificity.

Keywords: Discrete wavelet transform; Epilepsy in early childhood; Epileptic seizure; Hypsarrhythmia; Ictal; Interictal; Machine learning; Rhythms of the EEG Signal.

Присъединете се към нашата
страница във facebook

Най-пълната база данни за лечебни билки, подкрепена от науката

  • Работи на 55 езика
  • Билкови лекове, подкрепени от науката
  • Разпознаване на билки по изображение
  • Интерактивна GPS карта - маркирайте билките на място (очаквайте скоро)
  • Прочетете научни публикации, свързани с вашето търсене
  • Търсете лечебни билки по техните ефекти
  • Организирайте вашите интереси и бъдете в крак с научните статии, клиничните изследвания и патентите

Въведете симптом или болест и прочетете за билките, които биха могли да помогнат, напишете билка и вижте болестите и симптомите, срещу които се използва.
* Цялата информация се базира на публикувани научни изследвания

Google Play badgeApp Store badge