Abstract：
:Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% - 99.89%; precision: 91.90% - 99.45%; sensitivity: 91.61% - 99.53%; specificity: 91.60% - 99.96%; f1 score: 91.70% - 99.48%; and area under the curve: 0.9688 - 0.9998).
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Zheng L,Liao P,Luo S,Sheng J,Teng P,Luan G,Gao JHdoi
10.1109/TMI.2019.2958699subject
Has Abstractpub_date
2020-06-01 00:00:00pages
1833-1844issue
6eissn
0278-0062issn
1558-254Xjournal_volume
39pub_type
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