TY - JOUR
T1 - Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
AU - Siddiqui, Mohammad Khubeb
AU - Huang, Xiaodi
AU - Morales-Menendez, Ruben
AU - Hussain, Nasir
AU - Khatoon, Khudeja
N1 - Publisher Copyright:
© 2020, Springer-Verlag France SAS, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Includes bibliographical references
PY - 2020/10/24
Y1 - 2020/10/24
N2 - Epilepsy is one of the most prevalent neurological disorders. Its accurate detection is a challenge since sometimes patients do not experience any prior alert to identify a seizure. Electroencephalography (EEG) recordings are used for seizure detection, but these are usually of longer duration, and as a result, the behavior of the inherent data set is highly imbalanced. To detect seizures in such a scenario is a challenging task; using a typical classifier such as decision tree and decision forest can result in highly skewed class value (non-seizure), causing incorrect detection of epileptic patients. To solve this, a cost-sensitive learning method with a random forest was used. An algorithm that helps in seizure detection by penalizing the cost of a false negative concerning the duration of an EEG recording was proposed. The experimental results show that executing the classifier without penalty or inadequate penalties to the cost matrix is not a satisfactory solution. As a result, the algorithm provides up to 100% recall, which means all the seizure seconds are detected. The proposed method substantiates achieving higher actual seizure detection rates; the imposed penalty should be equal to the time duration of the EEG recordings (in seconds) for a patient. Hence, it can be potentially applied to the pre-consultation to the neurologist at the Outpatient Department for the actual seizure detection cases and refer them to the neurology department for further consultation.
AB - Epilepsy is one of the most prevalent neurological disorders. Its accurate detection is a challenge since sometimes patients do not experience any prior alert to identify a seizure. Electroencephalography (EEG) recordings are used for seizure detection, but these are usually of longer duration, and as a result, the behavior of the inherent data set is highly imbalanced. To detect seizures in such a scenario is a challenging task; using a typical classifier such as decision tree and decision forest can result in highly skewed class value (non-seizure), causing incorrect detection of epileptic patients. To solve this, a cost-sensitive learning method with a random forest was used. An algorithm that helps in seizure detection by penalizing the cost of a false negative concerning the duration of an EEG recording was proposed. The experimental results show that executing the classifier without penalty or inadequate penalties to the cost matrix is not a satisfactory solution. As a result, the algorithm provides up to 100% recall, which means all the seizure seconds are detected. The proposed method substantiates achieving higher actual seizure detection rates; the imposed penalty should be equal to the time duration of the EEG recordings (in seconds) for a patient. Hence, it can be potentially applied to the pre-consultation to the neurologist at the Outpatient Department for the actual seizure detection cases and refer them to the neurology department for further consultation.
KW - Class imbalance
KW - Classification
KW - Cost-sensitive learning
KW - Decision forest
KW - Epilepsy
KW - Epilepsy monitoring unit
KW - Scalp EEG
KW - Seizure detection
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U2 - 10.1007/s12008-020-00715-3
DO - 10.1007/s12008-020-00715-3
M3 - Article
AN - SCOPUS:85093985615
SN - 1955-2513
VL - 14
SP - 1491
EP - 1509
JO - International Journal on Interactive Design and Manufacturing
JF - International Journal on Interactive Design and Manufacturing
ER -