文献类型：期刊文献

英文题名：Detection of mental fatigue state with wearable ECG devices

作者：Huang, Shitong[1];Li, Jia[2];Zhang, Pengzhu[1];Zhang, Weiqiang[1]

机构：[1]Shanghai Jiao Tong Univ, Antai Coll Econ & Management, 1954 Huashan Rd, Shanghai 200030, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2018

卷号：119

起止页码：39

外文期刊名：INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

收录：;EI(收录号：20183705808962);WOS:【SSCI(收录号:WOS:000447628900005)，SCI-EXPANDED(收录号:WOS:000447628900005)】；

基金：This research was supported by the National Natural Science Foundation of China with grants (71371005, 71471064, and 91646205).

语种：英文

外文关键词：Mental fatigue; Wearable devices; ECG; HRV; Feature selection; Machine learning

摘要：Overwork-related disorders, such as cerebrovascular/cardiovascular diseases (CCVD) and mental disorders due to overwork, are a major occupational and public health issue worldwide, particularly in East Asian countries. Since wearable smart devices are inexpensive, convenient, popular and widely available today, we were interested in investigating the possibility of using wearable smart electrocardiogram (ECG) devices to detect the mental fatigue state. In total, 35 healthy participants were recruited from a public university in East China. Throughout the entire experiment, each participant wore a wearable device that was further linked to a smartphone to upload the data based on Bluetooth transmission. To manipulate the fatigue state, each participant was asked to finish a quiz, which lasted for approximately 80 min, with 30 logical referential and computing problems and 25 memory tests. Eight heart rate variability (HRV) indicators namely NN. mean (mean of normal to normal interval), rMSSD (root mean square of successive differences), PNN50 (the proportion of NN50 divided by total number of NNs), TP (total spectral power), HF (high frequency from 0.15 Hz to 0.4 Hz), LF (low frequency from 0.04 Hz to 0.15 Hz), VLF (very low frequency from 0.0033 Hz to 0.04 Hz) and the LF/HF ratio were collected at intervals of 5 min throughout the entire experiment. After the feature selection was performed, six indicators remained for further analysis, which were the NN. mean, rMSSD, PNN50, TP, LF, and VLF. Four algorithms, support vector machine (SVM), K-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR), were used to build classifiers that automatically detected the fatigue state. The best performance was achieved by KNN, which had a CV accuracy of 75.5%. The NN. mean, PNN50, TP and LF were the most important HRV indicators for mental fatigue detection. KNN performed the best among the four algorithms and had an average CV accuracy of 65.37% for all of the possible feature combinations.