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تحلیل الگوی باینری محلی سیگنالهای فشار پا جهت تشخیص سکته مغزی | ||
| مجله مهندسی برق دانشگاه تبریز | ||
| دوره 55، شماره 3 - شماره پیاپی 113، دی 1404، صفحه 623-631 اصل مقاله (665.58 K) | ||
| شناسه دیجیتال (DOI): 10.22034/tjee.2024.59207.4759 | ||
| نویسندگان | ||
| عارفه یعقوبی1؛ پیوند قادریان* 2 | ||
| 1دانشجوی کارشناسی ارشد، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران | ||
| 2دانشیار، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران | ||
| چکیده | ||
| در بیماران مبتلا به سکته مغزی بهصورت عمومی مشکلات حرکتی و راه رفتن قابل مشاهده است که کیفیت زندگی آنها را تحت تاثیر قرار میدهد. از اینرو تشخیص دقیق سکته مغزی برای ارائه یک راهکار درمانی و توانبخشی موثر در این بیماران ضروری به نظر میرسد. با این حال، توسعه یک ابزار تشخیصی کمهزینه و غیرتهاجمی برای کاربردهای کلینیکی یک چالش بزرگ در این زمینه محسوب میشود. به همین جهت، در این مطالعه یک روش تشخیصی جدید سکته ایسکمیک بر پایه ویژگیهای ساختاری سیگنال فشار کف پا و طبقهبند ماشین بردار پشتیبان ارائه شده است. در این روش، یک الگوی باینری محلی یکنواخت که از نمایش زمانی-فرکانسی سیگنال فشار کف پا استخراج شده است، برای اخذ ساختار محلی سیگنال در فضای دوبعدی و کمیسازی پایداری این الگو استفاده شده است. روش پیشنهادی به کمک سیگنالهای ثبت شده از 36 فرد سالم و 46 بیمار مبتلا به سکته ایسکمیک در حین راه رفتن طبیعی فرد مورد ارزیابی قرار گرفته است. جهت ارائه تحلیل ناحیهای، طبقهبندی با استفاده از کانالهای مختلف کف پا انجام شده است. نتایج بهدست آمده به میانگین صحت 99/66 درصد برای تشخیص سکته مغزی رسیده است. در ادامه، طی یک آزمایش مقایسهای، پایداری و عدم تغییر نتایج روش پیشنهادی در برابر سنسورهای فشار نواحی مختلف کف پا و پارامترهای تکنیکی روش الگوی باینری محلی نشان داده شده است. عملکرد روش پیشنهادی نشان میدهد که تحلیل الگوی باینری محلی سیگنال فشار کف پا قادر است افراد سالم و بیماران مبتلا به سکته مغزی را بهصورت موثری تفکیک نماید. | ||
| کلیدواژهها | ||
| یادگیری ماشین؛ سکته مغزی ایسکمیک؛ ویژگیهای زمانی-فرکانسی فشار کف پا؛ تشخیص اتوماتیک | ||
| مراجع | ||
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