北京生物醫(yī)學(xué)工程

基于BP神經(jīng)網(wǎng)絡(luò)的手功能康復(fù)評估研究

Research on hand function rehabilitation evaluation based on BP neural network

作者：谷雯雪王殊軼樊琳林晨琳呂燕飛

單位：上海理工大學(xué)醫(yī)療器械與食品學(xué)院（上海200093）威海市中醫(yī)院（山東威海 264200）

關(guān)鍵詞：手功能康復(fù); 神經(jīng)網(wǎng)絡(luò); 評估; 握力; 肌電

分類號：R318.04

出版年·卷·期（頁碼）：2020·39·6（622-626）

摘要：

目的針對手功能障礙康復(fù),本文提出了一種基于神經(jīng)網(wǎng)絡(luò)的主客觀結(jié)合的康復(fù)評估新方法。方法選取27例偏癱患者進行抓握測試，記錄患者的握力、捏力、肌電數(shù)據(jù)，采用Fugl-Meyer評定法(Fugl-Meyer assessment, FMA)得到患者的手功能康復(fù)評估分數(shù)。以握力和肌電等參數(shù)為輸入，以評估分數(shù)為輸出，建立基于BP(Back-Propagation)神經(jīng)網(wǎng)絡(luò)的手功能康復(fù)評估模型，利用獲取的24組樣本數(shù)據(jù)對神經(jīng)網(wǎng)絡(luò)進行訓(xùn)練，余下的3組樣本用于精度驗證，并通過聚類分析進一步提高預(yù)測精度。結(jié)果訓(xùn)練后，模型的平均評估誤差率為18.13%，通過調(diào)整樣本類數(shù)，平均評估誤差最大降至15.84%。結(jié)論評估模型可以較為準(zhǔn)確地預(yù)測評估分數(shù)，BP神經(jīng)網(wǎng)絡(luò)可以用于評估手功能障礙的康復(fù)程度。

Objective Aiming at the hand disfunction rehabilitation, a combination of the subjective and objective method of rehabilitation assessment based on neural network is proposed. Methods 27 patients with Hemiplegia were selected for the grip test. The gripping force, pinch force and electromyographic data of the patients were recorded. The Fugl-Meyer assessment (FMA) was used to obtain the hand function assessment scores. Using parameters such as grip strength and myoelectricity as input, and evaluation score as output, a hand function rehabilitation evaluation model based on BP (Back-Propagation) neural network was established, and the neural network was trained using the obtained 24 sets of sample data. The remaining 3 sets samples are used for accuracy verification, and cluster analysis further improves prediction accuracy. Results After training, the evaluation error rate of the model was 18.13% . By adjusting the number of sample classes, the maximum evaluation error was reduced to 15.84% . Conclusions The evaluation model can more accurately predict the evaluation score, and the BP neural network can be used to evaluate the degree of rehabilitation of hand dysfunction.

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