TY - JOUR
T1 - Using scalp EEG and intracranial EEG signals for predicting epileptic seizures
T2 - Review of available methodologies
AU - Usman, Syed Muhammad
AU - Khalid, Shehzad
AU - Akhtar, Rizwan
AU - Bortolotto, Zuner
AU - Bashir, Zafar
AU - Qiu, Haiyang
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.
AB - Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.
KW - Epilepsy prediction
KW - Intracranial EEG
KW - Preictal state
KW - Scalp EEG
KW - Seizures prediction methods
UR - http://www.scopus.com/inward/record.url?scp=85071480915&partnerID=8YFLogxK
U2 - 10.1016/j.seizure.2019.08.006
DO - 10.1016/j.seizure.2019.08.006
M3 - Article (Academic Journal)
C2 - 31479850
AN - SCOPUS:85071480915
SN - 1059-1311
VL - 71
SP - 258
EP - 269
JO - Seizure
JF - Seizure
ER -