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Supervised domain generalization for integration of disparate scalp EEG datasets for automatic epileptic seizure detection

Journal Article


Abstract


  • Numerous automatic epileptic seizure detectors (ESDs) with excellent performances have been reported, but they generally experience performance degradation when tested with real-life clinical data. This has been blamed on the scarcity of high-quality training data, which leads to models that generalize poorly. There is consequently interest in methods to improve the quality and quantity of training data for ESDs. This study used a domain generalization approach to combine data from two different datasets for training an ESD, which was thereafter tested on a third dataset. A subspace of the CHB-MIT and TUSZ scalp EEG seizure datasets was extracted using transfer component analysis, based on a reproducing kernel Hilbert space approach. We then used the Azimuthal Equidistant Projection to transform 3D electrode coordinates into 2D space, followed by interpolation using the Clough���Tocher technique to generate 16x16 rasters. We thereafter generated feature vectors, each of which was a sequence of 17 ten-layer 16x16 raster arrays. The vectors were used to train a recurrent-convolutional neural network. The network had a 128-unit long short-term memory layer with inputs from 17 parallel networks each with three stacks of convolutional layers. Testing was based on a private 26-subject dataset, combined with randomly selected subsets of the CHB-MIT and TUSZ datasets. A combined sensitivity of 74.5% was achieved, along with a false positive per hour rate of 0.84, and a latency of 2.32 s. Detection sensitivity on the private dataset was 72.5%. These results compare favorably with results of large-scale validation studies in literature and confirm the viability of this approach to increasing the size of training datasets for ESDs.

Publication Date


  • 2020

Citation


  • Ayodele, K. P., Ikezogwo, W. O., Komolafe, M. A., & Ogunbona, P. (2020). Supervised domain generalization for integration of disparate scalp EEG datasets for automatic epileptic seizure detection. Computers in Biology and Medicine, 120. doi:10.1016/j.compbiomed.2020.103757

Scopus Eid


  • 2-s2.0-85083420347

Volume


  • 120

Issue


Place Of Publication


Abstract


  • Numerous automatic epileptic seizure detectors (ESDs) with excellent performances have been reported, but they generally experience performance degradation when tested with real-life clinical data. This has been blamed on the scarcity of high-quality training data, which leads to models that generalize poorly. There is consequently interest in methods to improve the quality and quantity of training data for ESDs. This study used a domain generalization approach to combine data from two different datasets for training an ESD, which was thereafter tested on a third dataset. A subspace of the CHB-MIT and TUSZ scalp EEG seizure datasets was extracted using transfer component analysis, based on a reproducing kernel Hilbert space approach. We then used the Azimuthal Equidistant Projection to transform 3D electrode coordinates into 2D space, followed by interpolation using the Clough���Tocher technique to generate 16x16 rasters. We thereafter generated feature vectors, each of which was a sequence of 17 ten-layer 16x16 raster arrays. The vectors were used to train a recurrent-convolutional neural network. The network had a 128-unit long short-term memory layer with inputs from 17 parallel networks each with three stacks of convolutional layers. Testing was based on a private 26-subject dataset, combined with randomly selected subsets of the CHB-MIT and TUSZ datasets. A combined sensitivity of 74.5% was achieved, along with a false positive per hour rate of 0.84, and a latency of 2.32 s. Detection sensitivity on the private dataset was 72.5%. These results compare favorably with results of large-scale validation studies in literature and confirm the viability of this approach to increasing the size of training datasets for ESDs.

Publication Date


  • 2020

Citation


  • Ayodele, K. P., Ikezogwo, W. O., Komolafe, M. A., & Ogunbona, P. (2020). Supervised domain generalization for integration of disparate scalp EEG datasets for automatic epileptic seizure detection. Computers in Biology and Medicine, 120. doi:10.1016/j.compbiomed.2020.103757

Scopus Eid


  • 2-s2.0-85083420347

Volume


  • 120

Issue


Place Of Publication