تجزیه و تحلیل چندوجهی سیگنالهای الکتروکاردیوگرام برای تشخیص آریتمی قلبی با بهرهگیری از روشهای یادگیری ماشین و یادگیری عمیق | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| مقاله 2، دوره 3، شماره 1، شهریور 1404، صفحه 17-34 اصل مقاله (1.32 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2025.22930.1118 | ||
| نویسندگان | ||
| مطهره اکبری پودینه1؛ فاطمه زارع مهرجردی* 2؛ محسن سرداری3 | ||
| 1کارشناسی ارشد هوش مصنوعی، گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه میبد، میبد، ایران | ||
| 2استادیار گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه میبد، میبد، ایران | ||
| 3دانشیار گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه میبد، میبد، ایران | ||
| چکیده | ||
| بیماریهای قلبی مانند آریتمی قلبی شایعترین علت مرگ در جهان محسوب میشوند. تشخیص سریع این نوع بیماری باعث افزایش کیفیت زندگی، طول عمر و کاهش هزینههای درمان میشود. در این پژوهش هدف شناسایی بیماری آریتمی قلبی از روی الکتروکاردیوگرام و ابزار هوش مصنوعی است. روش پیشنهادی از سه مرحله پیشپردازش، تقسیمبندی پایگاه داده و طبقهبندی دادهها تشکیلشده است. ابتدا در مرحله پیشپردازش، عملیات نرمالسازی، پاکسازی و متوازنسازی کلاسها انجام شده است. سپس پایگاه داده پردازششده برای عملیات آموزش و آزمایش تقسیمبندی شده است. در نهایت دادهها با استفاده از طبقهبندهای مختلف یادگیری ماشین، معماریهای یادگیری عمیق و یک مدل ترکیبی از معماریهای CNN، RNN و Transformer، گروهبندی شدهاند. روش پیشنهادی با پایگاه داده MIT-BIH مورد ارزیابی قرار گرفته است. نتایج ارزیابیها نشان داد که از بین مدلهای یادگیری ماشین و معماریهای مختلف یادگیری عمیق، مدل ترکیبی با ادغام ویژگیهای محلی حاصل از معماری CNN و شناسایی وابستگیهای زمانی طولانی و پیچیده توسط معماری RNN و Transformer جز برترین طبقهبندها هست. در نهایت، یافتهها بر اهمیت ادغام ویژگیهای چندگانه در تحلیل سیگنالهای حاصل از الکتروکاردیوگرام برای تشخیص دقیقتر آریتمی قلبی تأکید میکند و میتواند در توسعه سیستمهای تشخیصی خودکار کارآمدتر استفاده شود. | ||
| کلیدواژهها | ||
| آریتمی قلبی؛ سیگنال ECG؛ یادگیری ماشین؛ یادگیری عمیق | ||
| عنوان مقاله [English] | ||
| Multimodal analysis of ECG signals for cardiac arrhythmia detection using machine learning and deep learning methods | ||
| نویسندگان [English] | ||
| Motahareh Akbari Podineh1؛ Fatemeh Zare Mehrjardi2؛ Mohsen Sardari Zarchi3 | ||
| 1MSc. in Artificial Intelligence, Computer Engineering Department, Faculty of Technology and Engineering, Meybod University, Meybod, Yazd, Iran | ||
| 2Assistant Professor, Computer Engineering Department, Faculty of Technology and Engineering, Meybod University, Meybod, Yazd, Iran | ||
| 3Associate Professor, Computer Engineering Department, Faculty of Technology and Engineering, Meybod University, Meybod, Yazd, Iran | ||
| چکیده [English] | ||
| Cardiovascular diseases, such as cardiac arrhythmia, are considered the most common cause of death worldwide. Early detection of this type of heart disease increases patient quality of life, prolongs life, and reduces treatment costs. In this research, the goal is to identify cardiac arrhythmia from electrocardiogram using artificial intelligence tools. The proposed method consists of three stages: preprocessing, database partitioning, and data classification. First, in the preprocessing stage, operations such as data normalization, cleaning, and balancing of classes have been performed. Then, the processed database has been partitioned for training and testing operations. Finally, the data has been classified using various machine learning classifiers, deep learning architectures, and a hybrid model combining CNN, RNN, and Transformer architectures. The proposed method has been evaluated using the MIT-BIH database. Evaluation results showed that among machine learning models and various deep learning architectures, the hybrid model, by integrating local features obtained from the CNN architecture and identifying long and complex temporal dependencies by the RNN and Transformer architectures, is among the top classifiers. Ultimately, the findings emphasize the importance of integrating multiple features in ECG signal analysis for more accurate cardiac arrhythmia diagnosis and can be used in the development of more efficient automated diagnostic systems. | ||
| کلیدواژهها [English] | ||
| Cardiac arrhythmia, ECG signal, machine learning, deep learning | ||
| مراجع | ||
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