Nguyen Linh Lan, Su Steven, Nguyen Hung T
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4475-8. doi: 10.1109/EMBC.2014.6944617.
Hyperglycemia or high blood glucose (sugar) level is a common dangerous complication among patients with Type 1 diabetes mellitus (T1DM). Hyperglycemia can cause serious health problems if left untreated such as heart disease, stroke, vision and nerve problems. Based on the electrocardiographic (ECG) parameters, we have identified hyperglycemic and normoglycemic states in T1DM patients. In this study, a classification unit is introduced with the approach of feed forward multi-layer neural network to detect the presences of hyperglycemic/normoglycemic episodes using ECG parameters as inputs. A practical experiment using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia is studied. Experimental results show that proposed ECG parameters contributed significantly to the good performance of hyperglycemia detections in term of sensitivity, specificity and geometric mean (70.59%, 65.38%, and 67.94%, respectively). From these results, it is proved that hyperglycemic events in T1DM can be detected non-invasively and effectively by using ECG signals and ANN approach.
高血糖或高血糖（血糖）水平是1型糖尿病（T1DM）患者中常见的危险并发症。如果不加以治疗，高血糖会导致严重的健康问题，如心脏病、中风、视力和神经问题。基于心电图（ECG）参数，我们已经识别出T1DM患者的高血糖和正常血糖状态。在本研究中，引入了一个分类单元，采用前馈多层神经网络方法，以心电图参数作为输入来检测高血糖/正常血糖发作的存在。对使用从西澳大利亚州政府卫生部收集的真实T1DM患者数据集进行的实际实验进行了研究。实验结果表明，所提出的心电图参数在敏感性、特异性和几何平均值方面（分别为70.59%、65.38%和67.94%）对高血糖检测的良好性能有显著贡献。从这些结果可以证明，通过使用心电图信号和人工神经网络方法，可以无创且有效地检测T1DM中的高血糖事件。
