IEEE Trans Biomed Eng 64(9):2003–2015Ĭhakrabarti S, Swetapadma A, Ranjan A, Pattnaik PK (2020) Time domain implementation of pediatric epileptic seizure detection system for enhancing the performance of detection and easy monitoring of pediatric patients. Neuroepidemiology 54(2):185–191īhattacharyya A, Pachori RB (2017) A multivariate approach for patient-specific eeg seizure detection using empirical wavelet transform. Biomed Phys Eng Express 3(1):015012īeghi E (2020) The epidemiology of epilepsy. Phys Rev E 64(6):061907īajaj V, Rai K, Kumar A, Sharma D (2017) Time-frequency image based features for classification of epileptic seizures from eeg signals. Sensors 20(9):2505Īndrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Biomed Signal Process Control 39:94–102Īlturki FA, AlSharabi K, Abdurraqeeb AM, Aljalal M (2020) Eeg signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. The highest accuracy and reduced computational complexity show the potential scope of proposed shifting sample differences in epilepsy diagnosis.Īlickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Accuracies of 99%, 98%, and 100% are obtained for normal vs. The proposed shifting sample difference method outperforms widely used discrete wavelet transform and empirical mode decomposition based features in three classification problems from the Bonn university epilepsy dataset. The epilepsy detection performances using five popular electroencephalogram features (statistical measures, Hjorth parameters, fractal dimensions, approximate entropy, and sample entropy) are investigated in this study. Unlike most recent seizure detection methods that use complex signal transformations, the shifting sample difference method is based on time-domain and does not require any transformation. This paper proposes a novel lightweight shifting sample difference method for efficient epileptic seizure detection from electroencephalogram signals.
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