Fatigue Detection in EEG Time Series Data Using Deep Learning

Abstract

Fatigue has widespread effects on the brain’s executive function, reaction time and information processing, causing loss of alertness, that affect safety, and productivity. There are various subjective and behavioural methods to measure fatigue. However, none of them is precise. The work in this thesis employs physiological measures such as heart rate, blood pressure, and breathing that are objective and quantitative indicators. These are thought to provide reliable measures of fatigue and may be easier to deploy in real world scenarios, compared to the subjective or behavioural methods. In particular, electroencephalogram (EEG) signals have the advantage of being able to measure fatigue in the early stages, and therefore have great potential in the design of early warning system to detect fatigue.

Traditional computational models trained using EEG data show potential improvement in detecting fatigue but require a significant number of electrodes, making deployment in a real-world fatigue detection scenario difficult (e.g., on a driver who is on the road). This project aims to develop computational models to perform fatigue detection using sparse EEG data from only two electrodes. The resulting algorithms could potentially be deployed in pragmatic situations (e.g., embedded in a wearable device), making the contribution of this study useful for real- world scenarios

In machine learning approaches, the area of deep learning has shown excellent performance in tackling problems of image classification and speech recognition. This project introduces the application of deep learning methods in early warning systems of fatigue detection. EEG data of patients suffering from mild to severe Obstructive Sleep Apnoea (OSA) are used in this study. These patients performed a driving simulation test under varying conditions of sleep deprivation, with their wake EEG and driving performance variables continuously monitored. The data collected during a driving simulation test of 57 sleep-deprived subjects were used for training and evaluating the computational models. The principal machine learning task was to employ the EEG data as input and predict the probability of a crash (crash / no crash) before the actual crash event.

After testing a preliminary EEG-K-Nearest Neighbour (EEG-KNN) as proof of concept to test data cleaning and pre-processing, two deep learning models were introduced, EEG-Deep Neural Network (EEG-DNN) and EEG Convolutional Neural Network (EEG-CNN). The Least Absolute Sum of Squares Operator (LASSO) was applied as a feature selection method in EEG-KNN to overcome the curse of dimensionality and identify promising features. EEG-KNN was used to predict a crash in the short-term (i.e., 5-second preduration), while EEG-DNN and EEG-CNN were used to predict a crash in the longer term (i.e., 6-minute pre-duration and 3- minute pre-duration respectively). Techniques such as dropout regularisation and early stopping were used to improve the performance of EEG-DNN and EEG-CNN on the test data.

The Receiver Operating Curve (ROC) is widely used to assess the performance of a classifier and compare the number of true positives (actual crash events) to the number of false positives. The metric considered for the evaluation of computational models on test data is the area under the ROC curve (AUROC). A larger value indicates better classification performance. The EEG-KNN in this study achieved an AUROC of 0.77 in short-term fatigue detection. The Deep learning model, EEG-DNN significantly improved the performance of crash prediction and achieved a sensitivity level of 87%. Further, the EEG-CNN was used to reduce the number of electrodes required to detect fatigue. The EEG-CNN achieved an AUROC of 0.95.

This project has developed a data framework and computational models to detect fatigue ahead of crash events, making intervention possible in the real-world scenarios. The proposed computational models utilised a lower number of electrodes and worked with sparse EEG data to detect fatigue, thus enabling a practical, effective and easy-to-use solution to be devised.