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Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
https://orcid.org/https://orcid.org/0000-0001-6699-6216
https://orcid.org/http://orcid.org/0000-0003-0498-1566
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.
Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
https://orcid.org/https://orcid.org/0000-0001-6699-6216
https://orcid.org/http://orcid.org/0000-0003-0498-1566
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.
Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
https://orcid.org/https://orcid.org/0000-0001-6699-6216
https://orcid.org/http://orcid.org/0000-0003-0498-1566
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.
Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
Int J Interact Des Manuf
Siddiqui, Mohammad Khubeb (author) / Huang, Xiaodi (author) / Morales-Menendez, Ruben (author) / Hussain, Nasir (author) / Khatoon, Khudeja (author)
2020-12-01
19 pages
Article (Journal)
Electronic Resource
English
Classification , Decision forest , Class imbalance , Cost-sensitive learning , Epilepsy , Seizure detection , Scalp EEG , Epilepsy monitoring unit Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
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