Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

Abstract

Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure onset zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

Figure 1

The color map representing the sLDA weights of the entropies with each subband for eight patients. The entropies represented in this paper are: APE (Approximate Entropy), PE (Permutation Entropy), Sh (Shannon Entropy), Sp (Sample Entropy), Ts (Tsallis Entropy), S2 (Phase Entropy 2), S1 (Phase Entropy 1), and Ren (Reny s Entropy).

Figure 2

Average AUC obtained from an individual entropy feature (bar) and its average sLDA weights across subbands (blue). Error bars indicate standard errors.

Figure 3

Color map representing the localization of segments (yellow spots) with respect to channels for the eight patients using our proposed method. The bar with each color map represents SOZ (red) and non-SOZ (black) with number of detected focal segments.

Figure 4

The 3D representation of the brain with interictal electrodes (yellow) of eight patients used from the dataset. The red circle represents the SOZ marked by epileptologists.

Figure 5

The different components of the proposed system for epileptic focus identification. The values $S_1,S_2,\dots ,S_N$ represent the subbands and N represents the total number of subbands.