Prediabetes is a metabolic condition before the occurrence of diabetes. The diagnosis of prediabetes can slow down or eliminate the growing cases of diabetes around the world. This paper presents a novel approach to identifying some vital physiological features for prediabetes prediction to stem the growing trend of type-2 diabetes. A standard OGTT experiment was conducted using BIOPAC 150MP, g-SAHARA and Mindray physiological device to capture continuous electrocardiogram (ECG) rhythm and electroencephalogram (EEG) of 40 human subjects while measuring blood glucose value at a regular interval. Features from the captured physiological signals were analyzed using an integrated space–time principal component analysis, independent component analysis, least absolute shrinkage and selector operator, and piecewise aggregate approximation techniques. The results from feature analysis show that certain features, namely HRV, QT, and ST from ECG; alpha, beta, and theta from the right parental hemisphere, along with alpha and delta from the left occipital hemisphere from EEG show significant correlation with change in the blood glucose. Furthermore, a weight-based multi-model was proposed by combining five (5) classification methods. The selected ECG and EEG features were applied for training the proposed multi-model classification, which is used to predict prediabetes. The evaluation of the multi-model performance produced accuracy, precision, and F1-measure of 92.0%, 88.8%, and 82.7% respectively, which is higher than the individual methods. The experimental results show that the coupling of multi-model electrophysiological data acquired with wearable multi-sensor devices can be utilized to diagnose diabetes early.

糖尿病前期是糖尿病发生前的一种代谢疾病。糖尿病前期的诊断可以减慢或消除世界范围内不断增长的糖尿病病例。本文提出了一种新颖的方法来鉴定一些重要的生理特征，以进行糖尿病前期预测，以阻止2型糖尿病的增长趋势。使用BIOPAC 150MP，g-SAHARA和Mindray生理设备进行了标准的OGTT实验，以捕获40名受试者的连续心电图（ECG）节律和脑电图（EEG），同时定期测量血糖值。使用集成的时空主成分分析，独立成分分析，最小绝对收缩和选择器算符以及分段聚合近似技术对捕获的生理信号中的特征进行了分析。特征分析的结果表明，某些特征，即心电图的HRV，QT和ST。右父母半球的α，β和θ以及EEG左枕半球的α和δ与血糖变化显着相关。此外，通过结合五（5）个分类方法，提出了基于权重的多模型。所选的ECG和EEG功能用于训练建议的多模型分类，该分类用于预测前驱糖尿病。对多模型性能的评估分别产生了92.0％，88.8％和82.7％的准确度，精密度和F1量度，高于各个方法。