TY - JOUR
T1 - A review of signal processing and machine learning techniques for interictal epileptiform discharge detection
AU - Abdi-Sargezeh, Bahman
AU - Shirani, Sepehr
AU - Sanei, Saeid
AU - Took, Clive Cheong
AU - Geman, Oana
AU - Alarcon, Gonzalo
AU - Valentin, Antonio
N1 - Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.
PY - 2024/1
Y1 - 2024/1
N2 - Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.
AB - Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.
KW - Humans
KW - Electroencephalography/methods
KW - Epilepsy/diagnosis
KW - Brain
KW - Seizures
KW - Machine Learning
KW - Scalp
U2 - 10.1016/j.compbiomed.2023.107782
DO - 10.1016/j.compbiomed.2023.107782
M3 - Review article
C2 - 38070202
SN - 0010-4825
VL - 168
SP - 107782
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
ER -