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Epileptic Seizure Detection Research Paper

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Seizure detection with Bigdata / Specific Problem, Gap

Different technologies are available for neuroimaging e.g. magnetic resonance imaging (MRI), functional magnetic resonance imaging (FMRI) and electroencephalography (EEG) etc. The epileptic patients are normally monitored in the neurophysiological clinics using EEG, a non-invasive, multichannel technology for recording brain’s activity. Commonly used approach for epileptic seizure detection is the analysis of scalp EEG [3]. The technology used for scalp EEG is getting better rapidly. The scalp EEG used in clinics are capable of producing data at sampling rate of 2Khz. Furthermore in some studies; the number of channels used increased from tens to thousands [4]. To have an idea of the amount of data, a continuous EEG monitoring of a patient at 256 Hz with 24 channels can approximately generate 1GB data per day. With higher sampling rate and increased number of channels, EEG can produce far more data, e.g. 500GB per day [1]. All these characteristics make processing of EEG a compute intensive and data intensive task.

Real time seizure detection …show more content…
E.g. in neurophysiological clinics, it is impractical for doctors to manually monitor hourly long EEG data of epileptic patients to observe for abnormalities. Another scenario is when the patient is on his/her own and gets a seizure. Automatic seizure detection in combination with alarm signal can be used to alert the patient or a relative regarding the seizure. In these scenarios we have to analyze the data as soon as it is generated, the strategy commonly known as online analysis. In online analysis we have to make decisions and provide results with negligible delay. Due to this reason, very complex and compute intensive algorithms cannot be used for seizure detection on real

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