Title: Optimizing Channel Selection for Seizure Detection,
Abstract: 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.