北京生物醫(yī)學(xué)工程

基于多元多尺度模糊熵的帕金森步態(tài)信號分類

Signal classification method for Parkinson gait based on multivariate multiscale fuzzy entropy

作者：王旭堯徐永紅

單位：燕山大學(xué)生物醫(yī)學(xué)工程研究所(河北秦皇島066004)

關(guān)鍵詞：模糊隸屬度函數(shù)；傳統(tǒng)多元多尺度熵；多元多尺度模糊熵；帕金森步態(tài)；分類

分類號：R318.04

出版年·卷·期（頁碼）：2016·35·5（489-496）

摘要：

目的傳統(tǒng)多元多尺度熵在量化有限長數(shù)據(jù)時會造成部分?jǐn)?shù)據(jù)丟失，同時傳統(tǒng)算法對閾值的過分依賴也會造成整個系統(tǒng)產(chǎn)生不穩(wěn)定的現(xiàn)象，二者皆會使最終結(jié)果產(chǎn)生較大的誤差，因此本文提出一種多元多尺度模糊熵算法。方法對傳統(tǒng)多元多尺度樣本熵的粗粒化方式進(jìn)行改進(jìn)，采用滑動均值濾波使粗粒化后各尺度上的時間序列與原始時間序列長度一致，減小了所計算多元多尺度熵的離散性。此外，本文算法在保持多元樣本熵中硬閾值優(yōu)點的同時，通過定義模糊隸屬度函數(shù)來統(tǒng)計兩復(fù)合延遲向量距離略大于閾值的情況。結(jié)果本算法既降低了傳統(tǒng)方法對閾值的依賴性，又很好地解決了傳統(tǒng)閾值所導(dǎo)致的不穩(wěn)定現(xiàn)象。最后用仿真數(shù)據(jù)對該算法進(jìn)行驗證，并將其應(yīng)用于帕金森患者步態(tài)復(fù)雜度的評價和分類。結(jié)論實驗結(jié)果表明多元多尺度模糊熵的識別效果明顯優(yōu)于傳統(tǒng)多元多尺度熵。

Objective Traditional multivariate multiscale entropy may cause some loss of data when quantifying finite length data. Meanwhile, the over-reliance of traditional algorithm on threshold may cause the entire system produces an unstable phenomenon. Both of the two problems can lead large errors in the results. Therefore, this paper presents multivariate multiscale fuzzy entropy. Methods The method improves coarse-grained way of the traditional algorithm and makes coarse-grained time series equal to the length of original time series on each scale by sliding mean filter, and reduces the discreteness of multivariate multiscale entropy. In addition, the algorithm maintains the advangtage of hard threshold in traditional method and counts the distance of two composite delay vectors slightly greater than threshold value by defining fuzzy membership function. Results This method not only reduces the dependence of threshold in traditional algorithm, but also solves the instability caused by traditional threshold. Finally, the algorithm is validated in the simulation data and is applied to the evaluation and classification on gait complexity of Parkinson patients. Conclusions The recognition effect of multivariate multiscale fuzzy entropy is better than traditional multivariate multiscale entropy.

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