1. An automated tool for reliable detection of seizures in rodent models of epilepsy
Armen Sargsyan1, Dmitri Melkonian1,
Pablo M. Casillas-Espinosa2, Terence J. O’Brien2
1 Kaoskey Pty. Ltd. Sydney, Australia
2 Department of Medicine,
The Royal Melbourne Hospital,
The University of Melbourne, Melbourne, Australia

2. in pre-clinical drug development of new therapies, eg
Prolonged video-EEG monitoring in chronic epilepsy rodent models has become an important tool
in pre-clinical drug development of new therapies, eg
anti-epileptogenesis,
disease modification and
drug resistant epilepsy.
Many researches still prefer to identify the seizures by themselves by visually inspecting the video and EEG
and tedious process
very time-consuming

3. Spectral Band Index Tool (SBIT)
An easy to use & reliable
computational tool
for detection of electrographic seizures
in prolonged EEG recordings
in rodent models of epilepsy
SBIT is based on advanced time-frequency analysis
detects the episodes of EEG with excessive activity
in certain frequency bands

4. The tool was tested on two acquired and two genetic spontaneously seizing, chronic epilepsy rat models
Animal models
Notation
Acquired
Post-status epilepticus model of temporal lobe epilepsy
Post-SE
Fluid percussion injury model of post-traumatic epilepsy
PTE
Genetic
Genetic Absence Epilepsy Rat from Strasbourg
GAERS
Wistar Albino Glaxo/Rijswijk model of absence epilepsy
WAG/Rij

5. The ictal EEG in these models contains a strong component with frequency around 21 Hz, which is specific for seizures only
SBI = max { Power spectrum in the frequency band [f1, f2] }
Spectral Band Index:
SBI calculated for frequency band 20 – 23 Hz
(narrow band around 21 Hz)
Post-SE, animal Z38, 12 days-long recording
12 days
Seizures
SBI calculated for frequency band 1 – 40 Hz
(wide band)

6. Examples of single and averaged spike-wave complexes and their amplitude frequency characteristics from four rat models
Average SW
(over one seizure)
0.00
0.05
0.10
Time, s
Model
Sample SW
0.00
0.05
0.10
Time, s 1 10
100
AFC
Peak frequency, Hz:
18.33
19.82
21.11
20.23
Frequency, Hz 20 1 10
100
AFC
17.64
24.12
19.21
20.29
Peak frequency, Hz:
Frequency, Hz 20
Post-SE
PTE
GAERS
WAG/Rij
Note that all peaks are within the Hz band, with mean around Hz

7. SBI plot for an artefact-free recording (model: Post-SE; animal: 27)
00:00
48:00
96:00
144:00
192:00
240:00
288:00
SBI, relative units
Time, hh:mm (12 days)
SBI plot for an artefact-free recording (model: Post-SE; animal: 27)
Seizure 2
Seizure 3
Seizure 4
Seizure 1
Largest inter-ictal SBI
00:00
48:00
96:00
144:00
192:00
240:00
288:00
SBI, relative units
Time, hh:mm (12 days)
SBI Plot for recording containing strong artefacts (model: Post-SE; animal: Z40)
Seizure 1
Seizure 2
Seizure 3
Seizure 4
Seizure 5
Artefacts

8. Processing results Number of animals Number of seizures Model
Annotated
Detected
Verified
Post-SE 39
120
124 (+4)
124
PTE 5 43
49 (+6) 49
GAERS 41
8733
8733
8733
WAG/Rij 14
825
825
825
Total 99
9721
9731
9731
All annotated seizures plus some missed by experts were detected (no false-negatives, 100% sensitivity)

9. Average reduction of processing time
Max processing time per 1 day record
5 min
Min processing time per 1 day record
0.1 min
Average processing time per 1 day record
1 min
Approximate time spent by expert for visual examination of 1 day record
60 min
60 times
Average reduction of processing time
(saves 98.3% of time)

10. Two major factors ensured such effective performance :
1. An advanced technique of short term spectral analysis based on original Similar Basis Function (SBF) algorithm, which, unlike conventional FFT
- is applicable to short time windows
- may calculate the Fourier transform within arbitrary frequency band of interest
- with arbitrary frequency resolution
2. Discovery of frequency component that is specific for rat ictal EEG

11. Conclusions SBIT significantly reduces processing time,
still leaving the final decisions to the expert
SBIT is reliable – no false negatives
SBIT is easy to use – the default parameters work well and there will be no or little need to change them when applied to rats
SBIT will be a substantial aid to researchers in seizure identification in long-term EEG recordings in rats
(e.g., pre-clinical research and therapy development)

12. Thank you Acknowledgments
We thank Prof. Gilles van Luijtelaar (Radboud University Nijmegen, The Netherlands ) for kindly providing the WAG/Rij rat data.