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Publication Type : Conference Paper
Publisher : IEEE
Source : 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019
Url : https://ieeexplore.ieee.org/document/8857588
Campus : Chennai
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods are encountered, it can result in microsleeps. Microsleeps are complete lapses in which a subject involuntarily stops responding to the task that they are currently performing due to temporary interruptions in visual-motor and cognitive coordination. Microsleeps can last up to 15 s while performing a particular task. In this study, the ability of a convolutional neural network CNN to detect microsleep states from 16-channel EEG data from 8 subjects, performing a 1D visuomotor was explored. The data were highly imbalanced. When averaged across 8 subjects there were 17 responsive states for every microsleep state. Two approaches were used to handle the CNN training with data imbalance - oversampling the minority class and cost-based learning. The EEG was analysed using a 4-s epoch with a step size of 0.25 s. Leave-one-subject-out cross-validation was used to evaluate the performance. The performance measures used for assessing the detection capability of the CNN were: sensitivity, precision, phi, geometric mean GM, AUC ROC , and AUC PR . The performance measures obtained using the oversampling and cost-based learning methods were: AUC ROC = 0.90/0.90, AUC PR = 0.41/0.41 and a phi = 0.42/0.40, respectively. Although the performances were similar, the cost-based learning method had a considerably shorter training time than the oversampling method.
Cite this Research Publication : V. Krishnamoorthy, R. Shoorangiz, S. J. Weddell, L. Beckert and R. D. Jones, "Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019