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Article Dans Une Revue Biomedical Signal Processing and Control Année : 2022

Classification of High Frequency Oscillations in intracranial EEG signals based on coupled time-frequency and image-related features

Résumé

High Frequency Oscillations (HFOs) occurring in the range of [30–500 Hz] in epileptic intracranial ElectroEncephaloGraphic (iEEG) signals have recently proven to be good biomarkers for localizing the epileptogenic zone. Identifying these particular cerebral events and their discrimination from other transient events like interictal epileptic spikes is traditionally performed by experts through a visual inspection. However, this is laborious, very time-consuming and subjective. In this paper, a new classification approach of HFOs is proposed. This approach mainly relies on the combination of raw time frequency (TF) features, computed from a TF representation of HFOs using S-transform, with relevant image-based ones derived from a binarization of the corresponding TF grayscale image. The obtained feature vector is then used to learn a multi-class Radial Basis Function (RBF) based Support Vector Machine (SVM) classifier. The efficiency of the proposed approach, compared to conventional classification schemes based only on time, frequency or energy-based features, is confirmed, using both simulated and real iEEG signals. The proposed classification system has achieved, using simulated data and a signal to noise ratio (SNR) of 15 dB, a sensitivity, specificity, accuracy, area under the curve and F1-score around 0.990, 0.996, 0.995, 0.993 and 0.990 respectively. Besides, for real data, our proposed approach has attained the scores of 0.765, 0.941, 0.906, 0.929 and 0.768 for sensitivity, specificity, accuracy, area under the curve and F1-score respectively. These results confirm the relevance of coupling TF and image-related features, in the way proposed in this paper, for higher HFOs classification quality compared to already existing approaches. © 2021 Elsevier Ltd
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Dates et versions

hal-03514821 , version 1 (08-04-2022)

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Paternité - Pas d'utilisation commerciale

Identifiants

Citer

F. Krikid, Ahmad Karfoul, S. Chaibi, A. Kachenoura, A. Nica, et al.. Classification of High Frequency Oscillations in intracranial EEG signals based on coupled time-frequency and image-related features. Biomedical Signal Processing and Control, 2022, 73, pp.103418. ⟨10.1016/j.bspc.2021.103418⟩. ⟨hal-03514821⟩
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