Epileptic focus location in the cerebral cortex using linear techniques and complex networks

Ramirez-Fuentes, Carlos A.; Barrera-Figueroa, V.; Tovar-Corona, Blanca; Silva-Ramirez, Martin Arturo; Jiménez, Laura Ivoone Garay

doi:10.1007/s11071-021-06418-y

Cited by 1 publication

(2 citation statements)

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“…In the studied sample, FNSZ episodes outnumbered other discharges. Although seizures can occur anywhere in the brain, the epileptic focal seizures generally occur at the seizure onset zone [ 44 , 45 , 46 ]. An ’epileptogenic zone’ is the region where epileptic seizures start or the part of the brain with the most ictal activity [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…Although seizures can occur anywhere in the brain, the epileptic focal seizures generally occur at the seizure onset zone [ 44 , 45 , 46 ]. An ’epileptogenic zone’ is the region where epileptic seizures start or the part of the brain with the most ictal activity [ 45 ]. The epileptogenic zones of FNSZ are typically located in the temporal and frontal lobes, which explains high MI values in the correspondent areas (see Figure 7 C).…”

Section: Resultsmentioning

confidence: 99%

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Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Statsenko,

Babushkin,

Talako

et al. 2023

Biomedicines

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Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

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