تعیین ویژگی متمایزکننده در پردازش سیگنال های مغزی: مطالعه موردی افراد معتاد به هروئین
عاطفه توبیها
1
(
دانشکده مهندسی برق، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
)
ندا بهزادفر
2
(
دانشکده مهندسی برق، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
)
محمد رضا یوسفی
3
(
دانشکده مهندسی برق، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
)
همایون مهدوی نسب
4
(
دانشکده مهندسی برق، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
)
غضنفر شاهقلیان
5
(
دانشکده مهندسی برق، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
)
الکلمات المفتاحية: اعتیاد, تحلیل سیگنال مغزی, شاخص دیویس-بولدین, طیف توان, هروئین,
ملخص المقالة :
این پژوهش با هدف شناسایی و تعیین ویژگیهای متمایزکننده سیگنالهای مغزی در افراد معتاد به هروئین انجام شده است. سیگنالهای الکتروانسفالوگرام (EEG) از 16 کانال مغزی برای 15 فرد معتاد و 15 فرد سالم جمعآوری شده و ویژگیهای فرکانسی و غیرفرکانسی آنها با استفاده از شاخص دیویس -بولدین ارزیابی شده است. نتایج بهدستآمده نشان میدهد که در افراد معتاد به هروئین، توان فرکانسی زیرباند آلفای باال در کانال 1O کاهش یافته و آنتروپی تقریبی در کانال Cz افزایش پیدا کرده است. برای طبقه بندی دادهها و تشخیص افراد معتاد از سالم، از طبقهبند ماشین بردار پشتیبان استفاده شده است. دقت و صحت تشخیص در ویژگی آنتروپی تقریبی به ترتیب برابر با %91.50 و %91.15 و در ویژگی توان طیفی زیرباند آلفای کانال 1O به ترتیب %95.92 و %92.18 بهدست آمده است. نتایج این مطالعه نشان میدهد که ویژگیهای منتخب نقش مؤثری در تشخیص افراد معتاد به هروئین دارند و تحلیل سیگنالهای مغزی میتواند به درک بهتر تأثیرات مصرف هروئین بر فعالیتهای مغزی و همچنین بهبود روشهای درمانی و پیشگیری از اعتیاد کمک کند.
Selecting appropriate and specific features in addicted and healthy individuals based on individual characteristics of heroin addicts or healthy individuals based on a feature selection method
Selecting a distinguishing feature from the Davis-Bouldin method
Determining a distinguishing feature for diagnosing addiction in brain signals
Examining time and frequency domain features to identify a distinguishing feature
Introducing a new database in diagnosing addiction based on brain signals
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