Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
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ORIGINAL PAPER
Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets Mohammad Khubeb Siddiqui1 Khudeja Khatoon4
· Xiaodi Huang2 · Ruben Morales-Menendez1
· Nasir Hussain3 ·
Received: 2 June 2020 / Accepted: 22 September 2020 © Springer-Verlag France SAS, part of Springer Nature 2020
Abstract 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. Keywords Classification · Decision forest · Class imbalance · Cost-sensitive learning · Epilepsy · Seizure detection · Scalp EEG · Epilepsy monitoring unit
1 Introduction
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Ruben Morales-Menendez [email protected] Mohammad Khubeb Siddiqui [email protected] Xiaodi Huang [email protected] Nasir Hussain [email protected] Khudeja Khatoon [email protected]
1
Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Ave. Garza Sada 2501, 64849 Monterrey, NL, Mexico
2
School of Computing and Mathematics, Charles Sturt University, Albury, Australia
3
College of Applied Studies and Community Service, King Saud University, Riyadh, Saudi Arabia
4
Hayat Unani Medical College and Research Centre, Lucknow, India
A brain consists of millions of neurons, and they communicate with each other in a standard pattern as in an electronic device such as computers [1]. Suppose the neurons cannot communicate due to any cause in the brain, such as blood clotting, physical injury, or genetic issues. In that case, the brain can be affected by different neurological disorders, and epilepsy is one [2]. Epileptic seizure detection is a challenging research topic. Statistics report that the prevalence of epilepsy is becoming one o
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