Denysyuk Hanna Vitaliyivna, Pinto Rui João, Silva Pedro Miguel, Duarte Rui Pedro, Marinho Francisco Alexandre, Pimenta Luís, Gouveia António Jorge, Gonçalves Norberto Jorge, Coelho Paulo Jorge, Zdravevski Eftim, Lameski Petre, Leithardt Valderi, Garcia Nuno M, Pires Ivan Miguel
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal.
Heliyon. 2023 Feb 10;9(2):e13601. doi: 10.1016/j.heliyon.2023.e13601. eCollection 2023 Feb.
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
心血管疾病在全球范围内的患病率正在上升。然而，技术在不断发展，利用低成本传感器可以随时随地进行监测。这一课题正在研究中，不同的方法能够自动识别这些疾病，为患者和医疗保健专业人员的治疗提供帮助。本文对利用心电图传感器进行疾病识别、分类和诊断进行了系统综述。该综述聚焦于2017年至2022年期间在不同科学数据库中发表的研究，这些数据库包括PubMed Central、Springer、Elsevier、多学科数字出版研究所（MDPI）、IEEE Xplore和Frontiers。最终对103篇科学论文进行了定量和定性分析。研究表明，网上有与各种疾病相关数据的不同数据集。研究中确定了几种基于机器学习/深度学习的模型，其中卷积神经网络和支持向量机是应用最广泛的算法。这篇综述能让我们确定可用于促进患者自主性的系统中的技术。
