Improved patient specific seizure detection during pre-surgical evaluation

Abstract

Objective There is considerable interest in improved off-line automated seizure detection methods that will decrease the workload of EEG monitoring units. Subject-specific approaches have been demonstrated to perform better than subject-independent ones. However, for pre-surgical diagnostics, the traditional method of obtaining a priori data to train subject-specific classifiers is not practical. We present an alternative method that works by adapting the threshold of a subject-independent to a specific subject based on feedback from the user. Methods A subject-independent quadratic discriminant classifier incorporating modified features based partially on the Gotman algorithm was first built. It was then used to derive subject-specific classifiers by determining subject-specific posterior probability thresholds via user interaction. The two schemes were tested on 529 h of intracranial EEG containing 63 seizures from 15 subjects undergoing pre-surgical evaluation. To provide comparison, the standard Gotman algorithm was implemented and optimised for this dataset by tuning the detection thresholds. Results Compared to the tuned Gotman algorithm, the subject-independent scheme reduced the false positive rate by 51% (0.23 to 0.11 h −1) while increasing sensitivity from 53% to 62%. The subject-specific scheme further improved sensitivity to 78%, but with a small increase in false positive rate to 0.18 h −1. Conclusions The results suggest that a subject-independent classifier scheme with modified features is useful for reducing false positive rate, while subject adaptation further enhances performance by improving sensitivity. The results also suggest that the proposed subject-adapted classifier scheme approximates the performance of the subject-specific Gotman algorithm. Significance The proposed method could potentially increase the productivity of offline EEG analysis. The approach could also be generalised to enhance the performance of other subject independent algorithms.