北京生物醫(yī)學(xué)工程

基于光電容積脈搏波的精神疲勞評(píng)估方法

Mental fatigue assessment method based on photoplethysmography signal

作者：余越嚴(yán)良文曹可樂(lè)

單位：上海大學(xué)機(jī)電工程與自動(dòng)化學(xué)院（上海 200444）<br />通信作者：嚴(yán)良文。E-mail: [email protected]

關(guān)鍵詞：疲勞評(píng)估；光電容積脈搏波；特征提取；機(jī)器學(xué)習(xí)；relief-F

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2023·42·1（45-51）

摘要：

目的針對(duì)精神疲勞難于定量評(píng)估的問(wèn)題，本文探索一種非侵入式可穿戴檢測(cè)方法獲取人體生理參數(shù)，從而實(shí)現(xiàn)對(duì)人體精神疲勞的定量評(píng)估。方法搭建光電容積脈搏波（photoplethysmography， PPG）采集平臺(tái)采集20名健康在校生的PPG信號(hào)，對(duì)PPG信號(hào)進(jìn)行預(yù)處理和特征提取，獲取時(shí)、頻域共143維特征。使用機(jī)器學(xué)習(xí)方法建立分類模型，對(duì)比特征選擇方法皮爾遜相關(guān)系數(shù)法、F檢驗(yàn)和relief-F得到特征權(quán)值，選擇最優(yōu)的特征子集，使用降維后的特征子集訓(xùn)練模型，減少?gòu)?fù)雜度和過(guò)擬合概率。結(jié)果與實(shí)際狀態(tài)對(duì)比，基于該方法的單個(gè)體疲勞檢測(cè)平均準(zhǔn)確率為92.48%，多個(gè)體疲勞檢測(cè)準(zhǔn)確率最大值為92.2%，可以有效地識(shí)別精神疲勞。結(jié)論光電容積脈搏波信號(hào)經(jīng)過(guò)時(shí)頻域分析構(gòu)建的特征能夠使用機(jī)器學(xué)習(xí)算法進(jìn)行準(zhǔn)確的精神疲勞狀態(tài)分類評(píng)估。

Objective In view of the difficulty in quantitative assessment of mental fatigue, this paper explores a non-invasive wearable detection method to obtain human physiological parameters, so as to achieve quantitative assessment of human mental fatigue. Methods PPG signals of 20 healthy school students were collected by photoplethysmography (PPG) acquisition platform. PPG signals were preprocessed and features were extracted. A total of 143 dimensional features were obtained in time and frequency domains. The machine learning method was used to establish the classification model, and the feature weights were obtained by comparing the feature selection method Pearson correlation coefficient method, F test and Relief -F. The optimal feature subset was selected, and the feature subset training model after dimension-reduction was used to reduce the complexity and overfitting probability. Results Compared with the actual state, the average accuracy of individual fatigue detection based on this method was 92.48%. The maximum accuracy of multi-individual fatigue detection is 92.2%, which can effectively identify mental fatigue. Conclusions The features constructed by time-frequency domain analysis of photoplethysmography signals can be used for accurate mental fatigue state classification assessment using machine learning algorithms.

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