Abstract
Accurate seizure prediction is important for design of wearable and implantable devices that can improve the lives of subjects with epilepsy. Such implantable devices can be used for closed-loop neuromodulation. However, there are many challenges that inhibit the performance of prediction models. One challenge in accurately predicting seizures is the nonstationarity of the EEG signals. This paper presents a patient-specific deep learning approach to improve predictive performance by transforming EEG data before extracting features for seizure prediction. In the proposed approach, a Sequence Transformer Network (STN) is first used to learn temporal and magnitude invariances in EEG data. The proposed method further computes the short-time Fourier transform (STFT) of the transformed EEG signals as input features to a convolutional neural network (CNN). A k-out-of-n post-processing method is used to reduce the significance of isolated false positives. The approach is tested using intracranial EEG from the American Epilepsy Society Seizure Prediction Challenge dataset. Leave-one-out cross validation is used to evaluate the model. The proposed model achieves an overall sensitivity of 82%, false prediction rate of 0.38/hour, and average AUC of 0.746.
Original language | English (US) |
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Title of host publication | 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6483-6486 |
Number of pages | 4 |
ISBN (Electronic) | 9781728111797 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico Duration: Nov 1 2021 → Nov 5 2021 |
Publication series
Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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ISSN (Print) | 1557-170X |
Conference
Conference | 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 |
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Country/Territory | Mexico |
City | Virtual, Online |
Period | 11/1/21 → 11/5/21 |
Bibliographical note
Funding Information:This research was supported in part by the National Science Foundation under grant number CCF-1954749.
Publisher Copyright:
© 2021 IEEE.