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- W4313492024 abstract "Epilepsy is a neurological condition characterized by recurrent seizures and affects millions of people all over the world. The abnormal brain electrical activity during an epileptic seizure can be seen with an EEG, which is then read by a trained medical professional to diagnose epilepsy. However, this is often time-consuming, expensive, inaccessible, and inaccurate, thus highlighting the need for automated epilepsy prediction. Previous algorithms for this problem only made use of small data sets which lacked variable, clinical grade data. We used the TUEP dataset to extract features through power spectral density and power spectral connectivity. These features were then classified into epileptic vs non-epileptic using a random forest classifier. Our feature extraction methods using power spectral density and spectral connectivity showed accuracies of over 90% in detecting epilepsy." @default.
- W4313492024 created "2023-01-06" @default.
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- W4313492024 date "2023-01-04" @default.
- W4313492024 modified "2023-09-29" @default.
- W4313492024 title "Automatic Epilepsy Detection from EEG signals" @default.
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- W4313492024 doi "https://doi.org/10.1145/3570991.3570995" @default.
- W4313492024 hasPublicationYear "2023" @default.
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