EITA Consulting, 5 Rue du Chant des Oiseaux, Montesson 78360, France.
MACS Research Laboratory RL16ES22, National Engineering School of Gabes, Gabes University, Gabes 6029, Tunisia.
Comput Intell Neurosci. 2022 Apr 28;2022:7617551. doi: 10.1155/2022/7617551. eCollection 2022.
Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achieving a spectacular performance in healthcare applications. According to the World Health Organization (WHO), in 2020 there were around 25.6 million people who died from cardiovascular diseases (CVD). Thus, this paper aims to shad the light on cardiology since it is widely considered as one of the most important in medicine field. The paper develops an efficient DL model for automatic diagnosis of 12-lead electrocardiogram (ECG) signals with 27 classes, including 26 types of CVD and a normal sinus rhythm. The proposed model consists of Residual Neural Network (ResNet-50). An experimental work has been conducted using combined public databases from the USA, China, and Germany as a proof-of-concept. Simulation results of the proposed model have achieved an accuracy of 97.63% and a precision of 89.67%. The achieved results are validated against the actual values in the recent literature.
如今，人工智能（AI）在医学诊断中的应用在学术文献和工业领域都引起了极大的关注。AI 包括深度学习（DL）模型，这些模型在医疗保健应用中取得了惊人的表现。根据世界卫生组织（WHO）的数据，2020 年全球有 2560 万人死于心血管疾病（CVD）。因此，本文旨在关注心脏病学，因为它被广泛认为是医学领域最重要的学科之一。本文开发了一种用于自动诊断 12 导联心电图（ECG）信号的高效 DL 模型，该模型有 27 个类别，包括 26 种 CVD 和正常窦性节律。所提出的模型由残差神经网络（ResNet-50）组成。已经使用来自美国、中国和德国的组合公共数据库进行了实验工作，作为概念验证。所提出模型的仿真结果达到了 97.63%的准确率和 89.67%的精度。所取得的结果与最近文献中的实际值进行了验证。
