A Software Toolset for Rapid Analysis of EEG Seizure and Video Data
1221Hans P. Harmon , Eric L. Akers , David A. Johnson , Erik Naylor , Jerome Clasadonte & Philip G. Haydon
111 - Pinnacle Technology Inc., Lawrence, KS
2 - Tufts University, Boston, MA

Introduction

Seizure and Video Synchronization• The seizure selection tool extracts
features from an existing seizure andplaces the event and feature set into adatabase.

• Because large variations in EEG waveforms are found between animals due to suchparameters as screw placement, brain morphology and animal size a majority ofresearchers still rely on human visual analysis of EEG data for accurate interpretationof seizure events.

• The extracted features are then used tomatch patterns and identify similar
seizures in other data sets (Figure 6).

• Analysis of long EEG datasets is very resource intensive, creating a hindrance tolarge-scale projects.
• To help streamline data analysis, we have developed a software toolset for rapid
analysis of EEG-based data.

• Features extracted include RMS power ofa specific frequency band or line length.

• The software includes detailed power spectrum analysis with adjustable Hanning andHamming windows along with customizable power band selection for all channels.
Once power spectra have been calculated for a specific dataset, colorized heat maps
and power frequency plots are available for visual analysis.
• Rapid identification of seizures within large datasets is accomplished based onparameters such as seizure length, specific frequency, root mean squared (RMS)
power and line length.

Seizure Selection• The features and the seizure waveform
itself are stored in a database for future
use in new analysis.
Figure 6: Seizure selected (in blue) and added to apattern matching database. Frequency selection
is shown in red.

Figure 3: Seizure recording with integrated, synchronized video playback• Identified seizure events can further be classified using embedded video and ratedusing standard Racine’s Scale parameters. After identification has been completed,aggregated seizure events are analyzed according to number, severity, frequency, andcircadian distribution over user-defined periods and adjustable for light/dark cycles.

• The seizure analysis software
displays the data, annotations,and a synchronization line for

Seizure Matching• The seizure matching tool uses the seizure
database to find similar seizure events andlist them for human review (Figure 7).

video (Figure 4).

Video Recording• Frequency data can also be
displayed and is updated inreal time based on the datasignal• Video can be recorded at varying resolutions (320 x 240,640 x 480), adjustable frame rates and from different
angles to meet the needs of any experiment including
chronic experiments lasting weeks (Figure 1).

• Pattern matching includes, but is notlimited to, RMS power and line length.
• All the events can be accepted, rejected,
or merged with nearby events to betterclassify seizures.

• A heat map of the data
spectrum plot displays timeand frequency of seizure
activity in red for rapid
identification of a seizure
event.

• Video frame to data point accuracy is less than 100 ms.
• Individual seizure events can be isolated and exported withvideo recording as .avi video clips for use in presentations
and papers.

Figure 4: Raw EEG Data (top), Spectral Plot Data (bottom left), andSpectrum Heat Map Plot (bottom right).
Figure 1: Video camera setup.

Custom Plot Generation• The seizure analysis tools provide a method of viewing and comparing various frequency
band and line length data as well as the raw EEG signal on a single time plot. Various
filters and scaling can be applied to the waveforms to help visualize the data (Figure 2).

Seizure Analysis• Theseizureanalysis
softwarecalculates average duration, averagetime between events, average RMS
power, and line length (Figure 5).
• The software constructs an aggregate
spectrum plot of all marked seizures
displaying the mean and variance.
• All calculations can be exported toASCII text files for use with 3rd party
analysis and statistical software.

Figure 7: Pattern matching a seizure event. A list of matched events (left). A stored seizure event (middle). Anda matched pattern in the EEG waveform (right).

Testing• Seizure detection was performed using
RMS power and line length analysis
separately on five individual mouse data
files. All files were compared to an
expert’s analysis (Figure 8).
Figure 8: Seizure detection compared to manual scoringConclusions• This software package provides comprehensive toolset for analysis and evaluation of
EEG and EMG seizure events.
• Tools for automated seizure detection can be used for rapid identification of seizure
events within large-scale datasets.

Figure 2: Custom plot displays - line length (orange), EEG theta (blue), and EMG RMS power (red). Seizure
events are easily identified as spikes.

Figure 5: Aggregate seizure spectrum (top). RMS power vs time (orange). Line length vs time (blue). Number ofseizures per unit time (red).

• Automated summary analysis tools provide researchers with rapid assessments of
their data in both graphical and ASCII formatted outputs.