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Automated Classification of Seizures against Nonseizures: A Deep Learning Approach | | Xinghua Yao
; Qiang Cheng
; Guo-Qiang Zhang
; | | Date: |
5 Jun 2019 | | Abstract: | In current clinical practice, electroencephalograms (EEG) are reviewed and
analyzed by well-trained neurologists to provide supports for therapeutic
decisions. The way of manual reviewing is labor-intensive and error prone.
Automatic and accurate seizure/nonseizure classification methods are needed.
One major problem is that the EEG signals for seizure state and nonseizure
state exhibit considerable variations. In order to capture essential seizure
features, this paper integrates an emerging deep learning model, the
independently recurrent neural network (IndRNN), with a dense structure and an
attention mechanism to exploit temporal and spatial discriminating features and
overcome seizure variabilities. The dense structure is to ensure maximum
information flow between layers. The attention mechanism is to capture spatial
features. Evaluations are performed in cross-validation experiments over the
noisy CHB-MIT data set. The obtained average sensitivity, specificity and
precision of 88.80%, 88.60% and 88.69% are better than using the current
state-of-the-art methods. In addition, we explore how the segment length
affects the classification performance. Thirteen different segment lengths are
assessed, showing that the classification performance varies over the segment
lengths, and the maximal fluctuating margin is more than 4%. Thus, the segment
length is an important factor influencing the classification performance. | | Source: | arXiv, 1906.2745 | | Services: | Forum | Review | PDF | Favorites | |
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