کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4943719 | 1437639 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Electroencephalogram signal classification based on shearlet and contourlet transforms
ترجمه فارسی عنوان
طبقه بندی سیگنال الکتروانسفالوگرام بر مبنای تغییرات شیب دار و کانورتور
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کلمات کلیدی
صرع، سیگنال الکتروانسفالوگرام، چاقوها، کانتورها،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World Health Organization (2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time-frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 67, January 2017, Pages 140-147
Journal: Expert Systems with Applications - Volume 67, January 2017, Pages 140-147
نویسندگان
Paulo Amorim, Thiago Moraes, Dalton Fazanaro, Jorge Silva, Helio Pedrini,