references.bib

@InProceedings{Baseer2017,
	Title                    = {Prediction of microsleeps using pairwise joint entropy and mutual information between {EEG} channels},
	Author                   = {A. Baseer and S. J. Weddell and R. D. Jones},
	Booktitle                = {39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
	Year                     = {2017},
	Month                    = {July},
	Pages                    = {4495--4498},
	
	Abstract                 = {Microsleeps are involuntary and brief instances of complete loss of responsiveness, typically of 0.5-15 s duration. They adversely affect performance in extended attention-driven jobs and can be fatal. Our aim was to predict microsleeps from 16 channel EEG signals. Two information theoretic concepts - pairwise joint entropy and mutual information - were independently used to continuously extract features from EEG signals. k-nearest neighbor (kNN) with k = 3 was used to calculate both joint entropy and mutual information. Highly correlated features were discarded and the rest were ranked using Fisher score followed by an average of 3-fold cross-validation area under the curve of the receiver operating characteristic (AUCROC). Leave-one-out method (LOOM) was performed to test the performance of microsleep prediction system on independent data. The best prediction for 0.25 s ahead was AUCROC, sensitivity, precision, geometric mean (GM), and φ of 0.93, 0.68, 0.33, 0.75, and 0.38 respectively with joint entropy using single linear discriminant analysis (LDA) classifier.},
	Doi                      = {10.1109/EMBC.2017.8037855},
	File                     = {:..//Articles/InProceedings//2017//3aicotieimabse_predictionmicrosleepsusing.pdf:PDF},
	ISSN                     = {1557-170X},
	Keywords                 = {electroencephalography;feature extraction;medical signal processing;sensitivity analysis;sleep;EEG channels;EEG signals;Fisher score;LDA classifier;LOOM;feature extraction;geometric mean;k-nearest neighbor;kNN;leave-one-out method;microsleeps;mutual information;pairwise joint entropy;receiver operating characteristics;single linear discriminant analysis;Electroencephalography;Entropy;Feature extraction;Gold;Mutual information;Standards;Target tracking},
	Owner                    = {Reza Shoorangiz},
	Timestamp                = {2017.10.25}
}

@InProceedings{Shoorangiz2016,
	Title                    = {Prediction of microsleeps from {EEG}: Preliminary results},
	Author                   = {R. Shoorangiz and S. J. Weddell and R. D. Jones},
	Booktitle                = {38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
	Year                     = {2016},
	Month                    = {Aug},
	Pages                    = {4650--4653},
	
	Abstract                 = {Brief episodes of momentarily falling asleep — microsleeps — can have fatal consequences, especially in the transportation sector. In this study, the EEG data of eight subjects, while performing a 1-D tracking task, were used to predict imminent microsleeps. A novel algorithm was developed to improve the accuracy of microsleep identification from two independent measures: tracking performance and face-video. The uncertain labels of gold-standard were then pruned out. Additionally, the state of microsleep at 0.25 s ahead was continuously predicted. Log-power spectral features were then extracted from EEG data. The most relevant features were selected by mutual information. Leave-one-subject-out was performed to test the classifier on an independent subject and this procedure was done for all the subjects. Two oversampling methods, synthetic minority oversampling technique (SMOTE) and adaptive sampling (ADASYN), were utilized to improve the training in the presence of imbalanced data. The best average area under the curve of receiver operating characteristic (AUCroc) of 0.90 was achieved using SMOTE oversampling over a 5.25 s window length, with a corresponding geometric mean (GM) of 0.74. ADASYN oversampling achieved the best sensitivity of 0.76 (cf. 0.70 for SMOTE), but with a lower specificity of 0.77 (cf. 0.86 for SMOTE).},
	Doi                      = {10.1109/EMBC.2016.7591764},
	File                     = {:..//Articles/InProceedings//2016//3aicotieimabs_predictionmicrosleepsfrom.pdf:PDF},
	Keywords                 = {Correlation;Electroencephalography;Feature extraction;Mutual information;Sleep;Target tracking;Training},
	Owner                    = {Reza Shoorangiz},
	Timestamp                = {2016.10.27}
}