EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes
- 10 December 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 39 (6), 1833-1844
- https://doi.org/10.1109/tmi.2019.2958699
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).Keywords
Funding Information
- National Natural Science Foundation of China (81790650, 81790651, 81727808, 81430037, 31421003)
- Beijing Municipal Science and Technology Commission (Z171100000117012)
- Shenzhen Peacock Plan (KQTD2015033016104926)
- Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team (2016ZT06S220)
- Shenzhen Science and Technology Research Funding Program (JCYJ20170412164413575)
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