Details

Autor(en) / Beteiligte

• Toth, Emilia
• Kumar, Sachin S
• Chaitanya, Ganne
• Riley, Kristen
• Balasubramanian, Karthi
• Pati, Sandipan

Titel

Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus

Ist Teil von

• Journal of neural engineering, 2020-11, Vol.17 (6), p.66004

Ort / Verlag

England: IOP Publishing

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• Objective. There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus-a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep). Approach. Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo-electroencephalography (EEG) evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states. Main results. 79 seizures (10 patients) and 158 segments (approx. 4 h) of interictal stereo-EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 s). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 s after seizure onset. The random forest (accuracy 93.9% and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states. Significance. These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.