Advanced Machine Learning Techniques For Arrhythmia Classification

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

With the development of Internet-of-Things (IoT) applications, the concept of smart healthcare applications has gradually emerged to be the main factor in medicine. In fact, this raises the need to have a secure system that is efficient at the same time, due to the limited resources of IoT devices. Many different techniques have been developed and studied recently. For example, with centralized learning (CL), all data are collected and processed in one place. But many of these models are heavy and lead to an infringement of patient's privacy. Hence, a Federated Learning (FL) approach helps in developing global application without storing the data in centralized cloud. Therefore, in this thesis, the concept of CL and FL using a convolutional neural (CNN) network is performed to identify and classify arrhythmia, while taking into consideration the accuracy and simplicity in simulating a system model that would be used in medical devices. The MIT-BIH dataset was used in this work to test and validate the proposed approach and compare it to other methods in the literature.

روابط وملفات

التعريف والنوع

رقم الوثيقة

0b3baf8d-8136-4425-8d26-883a76678f90

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2198915_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Abdulla Marawan Aboumadi","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Elias Yaacoub","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Advanced Machine Learning Techniques For Arrhythmia Classification

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Internet-of-Things (IoT) applications smart healthcare applications centralized learning (CL) Federated Learning (FL) convolutional neural (CNN) network

إشراف وإعداد

الإشراف

Elias Yaacoub

الإعداد

Abdulla Marawan Aboumadi

الاقتباسات الببليوغرافية