Optimizing Channel Selection for Seizure Detection
This work addresses artifact reduction in EEG-based seizure detection, but it is incremental as it primarily evaluates existing methods on different channel setups.
The study tackled the problem of artifact detection in EEG signals for seizure detection by testing a CNN-LSTM algorithm on reduced channel configurations, finding that systems with 8 to 20 channels achieved sensitivities of 33-37% with false alarms of 38-50 per 24 hours, compared to a baseline of 39% sensitivity and 23 false alarms with all 22 channels.
Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when fewer electrodes are used, less spatial information is available, making it harder to detect artifacts. In this study, we investigate the performance of a deep learning algorithm, CNN-LSTM, on several channel configurations. Each configuration was designed to minimize the amount of spatial information lost compared to a standard 22-channel EEG. Systems using a reduced number of channels ranging from 8 to 20 achieved sensitivities between 33% and 37% with false alarms in the range of [38, 50] per 24 hours. False alarms increased dramatically (e.g., over 300 per 24 hours) when the number of channels was further reduced. Baseline performance of a system that used all 22 channels was 39% sensitivity with 23 false alarms. Since the 22-channel system was the only system that included referential channels, the rapid increase in the false alarm rate as the number of channels was reduced underscores the importance of retaining referential channels for artifact reduction. This cautionary result is important because one of the biggest differences between various types of EEGs administered is the type of referential channel used.