ارائه یک روش مبتنی بر شبکه‌ی عصبی حافظه‌ی کوتاه‌مدت ماندگار برای تشخیص ناهنجاری در داده‌های سری زمانی نوار قلب

ملخص المقالة :

در این پژوهش، یک روش نوین برای تشخیص ناهنجاری در سیگنال‌های نوار قلب (ECG) ارائه شده است که مبتنی بر یادگیری عمیق و شبکه‌های عصبی حافظه‌ی کوتاه مدت ماندگار LSTM است. در این روش، ابتدا سیگنال‌های ECG دریافت شده از مجموعه‌داده MIT-BIH مورد پیش‌پردازش قرار گرفته‌اند. این پیش‌پردازش شامل حذف نویز، نرمال‌سازی و قطعه‌بندی سیگنال‌ها بوده است. سپس ویژگی‌های کلیدی از هر دوره فعالیت الکتریکی قلب مانند امواج P، Q، R، S، T و فواصل زمانی بین آن‌ها استخراج شده است. در ادامه، این ویژگی‌ها به‌عنوان ورودی به شبکه عصبی LSTM داده شده‌اند تا بتواند وابستگی‌های زمانی میان آن‌ها را یاد بگیرد. نتایج نشان می‌دهند که مدل پیشنهادی توانسته است با دقت بالا ریتم‌های نرمال و غیرنرمال را از یکدیگر تفکیک کند. این روش می‌تواند در کاربردهای پزشکی و پایش بلادرنگ سلامت قلب مورد استفاده قرار گیرد. مدل پیشنهادی ما با استفاده از تنها ۹ ویژگی و مدل LSTM، توانسته است 19 کلاس آریتمی را با دقت ۹۷.۳۳٪ شناسایی کند. این در حالی است که سایر روش‌ها با استفاده از ۶۰ ویژگی و مدل‌های پیچیده‌تر، دقت کمتری داشته‌اند.

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