Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.
Clin Neurophysiol
; 167: 14-25, 2024 Aug 24.
Article
in En
| MEDLINE
| ID: mdl-39265288
ABSTRACT
OBJECTIVE:
Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgroups affected performance, and which ioECoG features were most important.METHODS:
We included 91 ioECoG-guided epilepsy surgery patients with Engel 1A outcome. We allocated 71 training and 20 test set patients. We trained an extra trees classifier (ETC) with 14 spectral features to classify ioECoG channels as covering resected or non-resected tissue. We compared the ETC's performance with clinical ioECoG reading and assessed whether patient subgroups affected performance. Explainable artificial intelligence (xAI) unveiled the most important ioECoG features learnt by the ETC.RESULTS:
The ETC outperformed clinical reading in five test set patients, was inferior in six, and both were inconclusive in nine. The ETC performed best in the tumor subgroup (area under ROC curve 0.84 [95%CI 0.79-0.89]). xAI revealed predictors of resected (relative theta, alpha, and fast ripple power) and non-resected tissue (relative beta and gamma power).CONCLUSIONS:
Combinations of subtle spectral ioECoG changes, imperceptible by the human eye, can aid healthy and pathological tissue discrimination.SIGNIFICANCE:
ML with spectral ioECoG features can support, rather than replace, clinical ioECoG reading, particularly in tumors.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Clin Neurophysiol
/
Clin. neurophysiol
/
Clinical neurophysiology
Journal subject:
NEUROLOGIA
/
PSICOFISIOLOGIA
Year:
2024
Document type:
Article
Country of publication: