ECG-Based Driving Fatigue Detection Using Heart Rate Variability Analysis with Mutual Information

Abstract

One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.

Time Series Feature Selection Method Based on Mutual Information

“…Its main function is to reduce computational complexity, avoid the "curse of dimensionality" problem [1], reduce training time, and improve the performance of the predictor [2]. Therefore, how to effectively extract system features is one of the key issues in the field of time series analysis [3], which has been widely used in the following fields: image recognition [4,5], natural language processing [6,7], data mining [8,9], fault diagnosis [10][11][12], remaining useful life prediction [13,14], microbes classification [15], fatigue detection [16], image classification [17], intrusion detection [18][19][20][21], etc.…”