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

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

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

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

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)

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 17-25 Hz band, with mean around 20 -21 Hz

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

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)

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)

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

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)

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