本文關(guān)鍵詞：基于CEEMDAN-ELM的短期血糖預測模型研究　出處：《鄭州大學》2017年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 血糖預測 極限學習機 CEEMDAN 低血糖預警 CGM

【摘要】：由于生活方式的改變,糖尿病患病群體呈年輕化趨勢,已成為嚴重影響人類健康的疾病。血糖預測是人工胰臟血糖閉環(huán)控制的關(guān)鍵,可通過調(diào)控胰島素注射劑量和時間強化糖尿病患者體內(nèi)的血糖控制并降低并發(fā)癥的發(fā)生,為醫(yī)生和患者進行血糖控制提供數(shù)據(jù)支持。因此提高血糖預測精度、增加預測時間具有十分重要意義。血糖預測是根據(jù)人體內(nèi)的歷史血糖值來預測未來一段時間血糖濃度的變化趨勢。本文在研究已有的血糖預測技術(shù)基礎上,提出了一種基于自適應白噪聲完整聚合經(jīng)驗模態(tài)分解-極限學習機(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Extreme Learning Machine,CEEMDAN-ELM)的短期血糖預測模型。為提高血糖預測的精確度,模型首先運用信號分析技術(shù),利用CEEMDAN方法對糖尿病患者血糖值時間序列進行平穩(wěn)化處理,逐級分解患者血糖數(shù)據(jù)中存在的不同尺度下的波動或變化趨勢以降低血糖值時間序列的非線性和非平穩(wěn)性,獲得一組含有不同頻段特征的血糖分量;然后對各血糖分量分別利用ELM進行預測;最后融合各血糖分量的預測結(jié)果,獲得糖尿病患者最終的血糖預測值。論文在CEEMDAN-ELM短期血糖預測模型基礎上進行了低血糖預警研究,設計了一種新的低血糖預警方法。本文利用60例Ⅱ型糖尿病患者的血糖數(shù)據(jù)進行血糖預測實驗,并采用克拉克網(wǎng)格分析法、配對t檢驗等對預測模型的性能進行了檢驗,采用虛警率和漏警率來評估低血糖預警效果。結(jié)果表明:與ELM模型和EMD-ELM模型相比,CEEMDAN-ELM短期血糖預測模型提前45min的血糖預測達到了較高的精確度(60例患者的平均RMSE=0.2046,MAPE=2.0855%);提前45min的患者血糖預測值基本落在了Clarke網(wǎng)格誤差圖的A區(qū)域。在CEEMDAN-ELM短期血糖預測模型的基礎上提出的低血糖預警方法使得26例具有低血糖事件的糖尿病患者的虛警率為0.77%,漏警率為8.71%。CEEMDAN-ELM血糖預測模型提高了預測精度,延長了預測時間,對提高糖尿病的治療效果具有重要意義。
[Abstract]:Because of the change of life style, the diabetic patients tend to be younger, which has become a serious disease affecting human health. Blood glucose prediction is the key to the closed loop control of blood glucose in artificial pancreas. It can enhance the blood glucose control and reduce the incidence of complications in diabetic patients by regulating the dose and time of insulin injection, which can provide data support for doctors and patients to carry out blood glucose control. Therefore, improve the accuracy of blood glucose prediction. It is very important to increase the predicted time. The prediction of blood sugar is based on the historical blood sugar value in human body to predict the trend of blood glucose concentration in the future. An adaptive white noise complete aggregation empirical mode decomposition-extreme learning machine (LLMs) based on adaptive white noise is proposed in this paper. Complete Ensemble Empirical Mode Decomposition with Adaptive. Noise-Extreme Learning Machine. CEEMDAN-ELM) short-term blood glucose prediction model. In order to improve the accuracy of blood glucose prediction, the model first used signal analysis technology. The CEEMDAN method was used to stabilize the time series of blood glucose in diabetic patients. In order to reduce the nonlinearity and nonstationarity of the time series of blood glucose values, a group of blood glucose components with different frequency characteristics was obtained by decomposing the fluctuation or changing trend of different scales in the blood glucose data step by step. Then the blood glucose components were predicted by ELM. Finally, the final blood glucose prediction value of diabetic patients was obtained by combining the predicted results of each component of blood sugar. Based on the CEEMDAN-ELM short-term blood glucose prediction model, hypoglycemia prediction was studied in this paper. A new early warning method for hypoglycemia was designed in this paper. The blood glucose prediction experiment was carried out by using the blood glucose data of 60 patients with type 鈪，

本文編號：1422072