Graversen C, Frøkjaer J B, Brock C, Drewes A M, Farina D
Mech-Sense, Department of Gastroenterology & Radiology, Aalborg Hospital, DK-9000 Aalborg, Denmark.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5242-5. doi: 10.1109/EMBC.2012.6347176.
Diabetes mellitus (DM) is a multi-factorial and complex disease causing autonomic neuropathy and gastrointestinal symptoms in some patients. The neural mechanisms behind these symptoms are poorly understood, but it is believed that both peripheral and central mechanisms are involved. To gain further knowledge of the central mechanisms, the aim of this study was to identify biomarkers for the altered brain activity in type-1 DM patients compared to healthy volunteers (HV), and to correlate the obtained biomarkers to clinical patient scores. The study included 14 DM patients and 15 HV, with brain activity recorded as multi-channel electroencephalography evoked brain potentials (EPs) elicited by painful electrical stimulations in the esophagus. The single-sweep EPs were decomposed by an optimized discrete wavelet transform (DWT), and averaged for each channel. The DWT features from the DM patients were discriminated from the HV by a support vector machine (SVM) applied in regression mode. For the optimal DWT, the discriminative features were extracted and the SVM regression value representing the overall alteration of the EP was correlated to the clinical scores. A classification performance of 86.2% (P=0.01) was obtained by applying a majority voting scheme to the 5 best performing channels. The biomarker was identified as decreased theta band activity. The regression value was correlated to symptoms reported by the patients (P=0.04). The methodology is an improvement of the present approach to study central mechanisms in diabetes mellitus, and may provide a future application for a clinical tool to optimize treatment in individual patients.
糖尿病（DM）是一种多因素的复杂疾病，在一些患者中会导致自主神经病变和胃肠道症状。这些症状背后的神经机制尚不清楚，但据信外周和中枢机制都有涉及。为了进一步了解中枢机制，本研究的目的是确定1型糖尿病患者与健康志愿者（HV）相比大脑活动改变的生物标志物，并将获得的生物标志物与临床患者评分相关联。该研究纳入了14名糖尿病患者和15名健康志愿者，通过食管疼痛电刺激诱发的多通道脑电图诱发电位（EPs）记录大脑活动。单扫EPs通过优化的离散小波变换（DWT）进行分解，并对每个通道进行平均。通过应用回归模式的支持向量机（SVM）将糖尿病患者的DWT特征与健康志愿者的特征区分开来。对于最佳DWT，提取判别特征，并将代表EP总体改变的SVM回归值与临床评分相关联。通过对5个表现最佳的通道应用多数投票方案，获得了86.2%（P = 0.01）的分类性能。生物标志物被确定为θ波段活动降低。回归值与患者报告的症状相关（P = 0.04）。该方法是对目前研究糖尿病中枢机制方法的改进，可能为临床工具在个体患者中优化治疗提供未来应用。
