Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
School of Kinesiology, Nutrition and Food Science, Rongxiang Xu College of Health and Human Services, California State University, Los Angeles, 5151 State University Dr., Los Angeles, CA 90032, USA.
Sensors (Basel). 2020 Apr 30;20(9):2556. doi: 10.3390/s20092556.
The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model-referred to as IGRNet-is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.
糖尿病前期的临床症状较轻，容易被忽视，但如果不进行早期干预，糖尿病前期可能会发展为糖尿病。在这项研究中，开发了一种深度学习模型——称为 IGRNet，它可以使用持续 5 秒的 12 导联心电图（ECG）以非侵入性、实时的方式有效地检测和诊断糖尿病前期。在搜索到合适的激活函数后，我们比较了两种主流的深度神经网络（AlexNet 和 GoogLeNet）和三种传统的机器学习算法，以验证我们方法的优越性。在包括混合组在内的独立测试集中进行测试后，IGRNet 的诊断准确率为 0.781，接受者操作特征曲线（AUC）下面积为 0.777。此外，在正常体重范围内测试集中的准确率和 AUC 分别为 0.856 和 0.825。实验结果表明，IGRNet 使用 ECG 对糖尿病前期进行诊断具有很高的准确性，优于现有的其他机器学习方法；这表明它有潜力作为一种非侵入性的糖尿病前期诊断技术应用于临床实践。
