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Infantile Spasms (ISS) characterized by electroencephalogram (EEG) recordings exhibiting hypsarrythmia (HYPS) are a severe form of epilepsy. Many clinicians have been trying to improve ISS outcomes; however, quantification of discharges from hypsarrythmic EEG readings remains challenging. To solve this issue, this paper describes a novel method that assists clinicians to successfully localize the epileptic discharges associated with ISS in HYPS. The approach includes: construct the time-frequency domain (TFD) of the EEG recording using matching pursuit TFD, decompose the TFD matrix into two submatrices using non-negative matrix factorization, and employ the decomposed vectors to locate the spikes. Performance evaluations showed results based on classification techniques: thresholdings, and support vector machine (SVM). Using the thresholdings, average true positive (TP) and false negative (FN) percentages of 86% and 14% were achieved, while a clinical software (Persyst) detected spikes (TP = 4%, FN = 96%). Using SVM, the percentage of area under curve (AUC) of receiver operating characteristic (ROC) was significantly improved up to 98.56%.

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Supachan Traitruengsakul received B.Eng. from King Mongkut Institute Technology Ladkrabang, Thailand, in 2007, and M.Sc. from Rochester Institute of Technology, Rochester, NY, in 2015. His interest is signal and image processing, machine learning, and dimensionality reduction. He works as an instructor at Rachasuda Collage Mahidol University.

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