Detection of mental fatigue state with wearable ECG devices

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2018-11, Vol.119, p.39-46
• Main Authors: Huang, Shitong, Li, Jia, Zhang, Pengzhu, Zhang, Weiqiang
• Format: Article
• Language: English
• Subjects: ECG; Feature selection; HRV; Machine learning; Mental fatigue; Wearable devices
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2018.08.010

•This paper aims to investigate 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.•Eight heart rate variability (HRV) indicators were collected at intervals of 5 min throughout the entire experiment.•Four algorithms, support vector machine (SVM), K-nearest neighbor (KNN), naïve Bayes (NB), and logistic regression (LR), were used to build classifiers that automatically detected the fatigue state. The best performance was achieved by KNN.•The NN.mean (mean of normal to normal interval), PNN50 (the proportion of NN50 divided by total number of NNs), TP (total spectral power), and LF (low frequency from 0.04 Hz to 0.15 Hz) were the most important HRV indicators for mental fatigue detection. 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, P