University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France.
SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France.
Sensors (Basel). 2022 Jun 29;22(13):4890. doi: 10.3390/s22134890.
(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.
(1) 背景：糖尿病（DM）是一种以血糖升高为特征的慢性代谢性疾病。最近，一些研究通过分析心血管系统参数的变化来研究糖尿病护理领域。事实上，心血管疾病是糖尿病患者的首要死亡原因。由于心电图（ECG）和光电容积脉搏波（PPG）信号具有成本效益高、使用方便等优点，最近已被用于糖尿病检测、血糖估计和糖尿病相关并发症检测。本综述的目的是详细概述所有已发表的方法，从传统（非机器学习）方法到深度学习方法，以使用 PPG 和 ECG 信号检测和管理糖尿病。本综述将使研究人员能够比较和理解每种方法在结果、数据量和复杂性方面的差异。
(2) 方法：我们基于专注于使用 ECG 和 PPG 信号进行糖尿病护理的文章进行了系统综述。搜索重点是与主题相关的关键词，如“糖尿病”、“ECG”、“PPG”、“机器学习”等。这是使用数据库（如 PubMed、Google Scholar、Semantic Scholar 和 IEEE Xplore）完成的。本综述的目的是详细概述所有已发表的方法，从传统（非机器学习）方法到深度学习方法，以使用 PPG 和 ECG 信号检测和管理糖尿病。本综述将使研究人员能够比较和理解每种方法在结果、数据量和复杂性方面的差异。
(3) 结果：共纳入 78 项研究。所选研究大多集中于血糖估计（41 项）和糖尿病检测（31 项）。只有 7 项研究侧重于糖尿病并发症检测。我们按方法呈现这些研究：传统方法、机器学习方法和深度学习方法。
(4) 结论：ECG 和 PPG 在糖尿病护理中的分析显示出非常有前景。为了将这些技术应用于临床环境，需要改进临床验证和数据处理标准化。
