Labeler-hot Detection of EEG Epileptic Transients
This work addresses the challenge of overlooked epileptic events in EEG diagnosis for epilepsy patients, offering an incremental improvement in detection methods.
The paper tackled the problem of detecting epileptic transients in EEG signals by integrating labeler category information into the event descriptor, which improved generalization performance over consensus-trained detectors, as shown in quantitative experiments on infant EEG recordings.
Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event detection algorithms has relied on ground truth labels, obtained from the consensus of the majority of labelers. In this work, we go beyond labeler consensus on EEG data. Our event descriptor integrates EEG signal features with one-hot encoded labeler category that is a key to improved generalization performance. Notably, boosted decision trees take advantage of singly-labeled but more varied training sets. Our quantitative experiments show the proposed labeler-hot epileptic event detector consistently outperforms a consensus-trained detector and maintains confidence bounds of the detection. The results on our infant EEG recordings suggest datasets can gain higher event variety faster and thus better performance by shifting available human effort from consensus-oriented to separate labeling when labels include both, the event and the labeler category.