DeepAI AI Chat
Log In Sign Up

Optimizing Channel Selection for Seizure Detection

by   Vinit Shah, et al.

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 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 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.


page 1

page 2

page 3

page 4


Improved EEG Classification by factoring in sensor topography

Electroencephalography (EEG) serves as an effective diagnostic tool for ...

Robust learning from corrupted EEG with dynamic spatial filtering

Building machine learning models using EEG recorded outside of the labor...

Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG

Biomedical decision making involves multiple signal processing, either f...

A channel attention based MLP-Mixer network for motor imagery decoding with EEG

Convolutional neural networks (CNNs) and their variants have been succes...

Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis

Electroencephalogram (EEG) signals are effective tools towards seizure a...

Driver fatigue EEG signals detection by using robust univariate analysis

Driver fatigue is a major cause of traffic accidents and the electroence...

Protocol-independent Detection of "Messaging Ordering" Network Covert Channels

Detection methods are available for several known covert channels. Howev...