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- W4386198414 abstract "Epilepsy is the main neurological disorder, which leads to human brain working incorrectly. Hundreds of thousands of people are stricken by this sickness. The random nature of seizure occurrence makes it vulnerable. The present work is direction of building any such gadget, which can supply a caution message to take a safety degree earlier than the seizure happens. For this, we used epileptic EEG signals. Following a thorough assessment, we unveiled several distinctive characteristics exhibited by epileptic EEG signals, including (i) background disruption, (ii) well-defined spike segments, (iii) electro cerebral negativity, and (iv) electrical field. We have designed an algorithmic model considering all of the above mentioned characteristics of epilepsy. By issuing an advance warning message, this model can provide information about the occurrence of predisposed seizure waveform from a specific part of the EEG signal. This will offer enormous help to the doctors in detecting the seizure from the pre-ictal, ictal, and inter-ictal parts of the EEG signal. This automated system provides a good handy solution to doctors and healthcare professionals. This system has been tested on various datasets and achieved 91.07% sensitivity, 97.37% specificity, and 99.03% positive predictive value and 92.67% accuracy for primary dataset, and in case of the secondary dataset, the proposed method achieved 100%, 92%, and 100% accuracy for the ictal part, pre-ictal, and inter-ictal parts, respectively, for the considered datasets which is clinically acceptable." @default.
- W4386198414 created "2023-08-27" @default.
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- W4386198414 date "2023-01-01" @default.
- W4386198414 modified "2023-09-25" @default.
- W4386198414 title "α and β-Testing of an Epileptic Seizure Detection Algorithm on Pre-ictal, Ictal, and Inter-ictal Part of EEG Signal" @default.
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- W4386198414 doi "https://doi.org/10.1007/978-981-99-3734-9_21" @default.
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