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
)
کلید واژه: اعتیاد, تحلیل سیگنال مغزی, شاخص دیویس-بولدین, طیف توان, هروئین,
چکیده مقاله :
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.
چکیده انگلیسی :
This study aims to identify and determine distinguishing features of brain signals in heroin-addicted individuals. Electroencephalogram (EEG) signals were collected from 16 brain channels for 15 addicted and 15 healthy individuals. Frequency and non-frequency features were evaluated using the Davies-Bouldin index. The results indicate that in heroin-addicted individuals, the frequency power in the upper alpha sub-band of the O1 channel decreased, while the approximate entropy in the Cz channel increased. To classify the data and distinguish addicted individuals from healthy ones, a Support Vector Machine (SVM) classifier was employed. The accuracy and precision of detection for approximate entropy were 91.50% and 91.15%, respectively, while for the upper alpha power of the O1 channel, they were 95.92% and 92.18%, respectively. The findings confirm the significance of selected features in distinguishing heroin-addicted individuals. The analysis of brain signals can provide a deeper understanding of the effects of heroin use on brain activity and contribute to improving treatment strategies and addiction prevention.
انتخاب ویژگی مناسب و اختصاصی در افراد معتاد و سالم بر اساس ویژگیهای فردی معتادان به هروئین و یا افراد سالم مبتنی بر یک روش انتخاب ویژگی
انتخاب ویژگی متمایز کننده از روش دیویس بولدین
تعیین ویژگی متمایز کننده برای تشخیص اعتیاد در سیگنالهای مغزی
بررسی ویژگیهای حوزه زمان و فرکانس برای شناسایی ویژگی متمایز کننده
معرفی یک پایگاه داده جدید در تشخیص اعتیاد مبتنی بر سیگنالهای مغزی
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