Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp
Jessica K Nadalin1, Uri T Eden2, Xue Han3
1Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States.
Background:
A reliable biomarker to identify cortical tissue responsible for generating epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike ripple events are a promising biomarker for epileptogenic tissue that currently require expert review for accurate identification. This expert review is time consuming and subjective, limiting reproducibility and high-throughput applications.
New Method:
To address this limitation, we develop a fully-automated method for spike ripple detection. The method consists of a convolutional neural network trained to compute the probability that a spectrogram image contains a spike ripple.
Results:
We validate the proposed spike ripple detector on expert-labeled data and show that this detector accurately separates subjects with low and high seizure risks.
Comparison With Existing Method:
The proposed method performs as well as existing methods that require manual validation of candidate spike ripple events. The introduction of a fully automated method reduces subjectivity and increases rigor and reproducibility of this epilepsy biomarker.
Conclusion:
We introduce and validate a fully-automated spike ripple detector to support utilization of this epilepsy biomarker in clinical and translational work.
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