14 COMMUN. BIOMATH. SCI., VOL. 4, NO. 1, 2021, PP. 14-22 Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges S. Gunadharma 1 , A. Rizal 1 , R. Ruslami 2 , T. H. Achmad 3 , S. S. Ju 4 , J. W. Puspita 5,6,* , S. W. Indratno 5 , E. Soewono 5 1 Department of Neurology Hasan Sadikin Hospital-Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia 2 Department of Pharmacology and Therapy, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia 3 Department of Biochemistry, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia 4 Department of Neurology Singapore General Hospital, Singapore 168753, Singapore 5 Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung, Indonesia 6 Faculty of Mathematics and Natural Science, Universitas Tadulako, Palu, Indonesia Email: * juni.wpuspita@yahoo.com Abstract A number of benign EEG patterns are often misinterpreted as interictal epileptiform discharges (IEDs) because of their epileptiform appearances, one of them is wicket spike. Differentiating wicket spike from IEDs may help in preventing epilepsy misdiagnosis. The temporal location of IEDs and wicket spike were chosen from 143 EEG recordings. Amplitude, duration and angles were measured from the wave triangles and were used as the variables. In this study, linear discriminant analysis is used to create the formula to differentiate wicket spike from IEDs consisting spike and sharp waves. We obtained a formula with excellent accuracy. This study emphasizes the need for objective criteria to distinguish wicket spike from IEDs to avoid misreading of the EEG and misdiagnosis of epilepsy. Keywords: epilepsy, interictal epileptiform discharges, wicket spike. 2010 MSC classification number: 60G35, 00A69. 1. I NTRODUCTION Scalp electroencephalography (EEG) is a recording technique for the spontaneous electrical activity of the brain taken from the scalp, which is then correlated with to the underlying brain function [1]. Electroen- cephalography (EEG) is the most frequently used test for epilepsy patients [2], [3]. This technique can be used to diagnose epilepsy and determine the seizure disorder type and its place of origin [3]. Interictal Epileptiform Discharges (IEDs) has become the hallmark for epilepsy, with the ability to distinctly identify cortical hyperexcitability and hypersynchrony, which are present in the interictal state [1], [4]. One should be aware that in the evaluation of abnormalities in the EEG, many EEG transients that morphologically resemble epileptiform discharges and that need to be distinguished from diagnostically crucial epileptiform abnormalities to avoid overdiagnosis or misdiagnosis. These include benign epileptiform variants that must be recognized. Although morphologically similar, they are non-epileptogenic with no established relationship with the process responsible for generating epileptic seizures [1]. Misdiagnosis of epilepsy is relatively common. The main occurrence of misdiagnosis is the overinterpre- tation of normal EEG patterns as epileptiform [5]. There is enough evidence showing misinterpretation of benign EEG discharges, such as wicket spikes, which may result in misdiagnosis of epilepsy [6]. About 54% of wicket rhythms were incorrectly interpreted as an epileptiform activity. Whereas interobserver reliability is high in trained individuals, it is probably much less so in the real world, and EEG interpretation errors are not uncommon [4]. Some algorithms created from mathematical design have found, automated detection of interictal spikes with a positive predictive value of 92% and a sensitivity of 82% and different artifacts in the scalp EEG using Walsh- transformed EEG signals [7], [8]. Seizure prediction also has been designed by using an automatic Artificial Neural Network-Aided Diagnosis (ANNAD) system based on mathematical study for initial scalp *Corresponding author Received September 29 th , 2020, Revised December 15 th , 2020, Accepted for publication March 30 th , 2021. Copyright ©2021 Published by Indonesian Biomathematical Society, e-ISSN: 2549-2896, DOI:10.5614/cbms.2021.4.1.2