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Sleep Analysis Software SleepSign for Animal

We are developing a New Automatic Scoring Algorithm! Algorithm for sleep scoring in experimental animals based on fast Fourier transform power spectrum analysis of the electroencephalogram Collaborating with Osaka Bioscience Institute, Japan. Sleep Analysis Software SleepSign for Animal.

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Sleep Analysis Software SleepSign for Animal

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  1. We are developing a New Automatic Scoring Algorithm! Algorithm for sleep scoring in experimental animals based on fast Fourier transform power spectrum analysis of the electroencephalogram Collaborating with Osaka Bioscience Institute, Japan Sleep Analysis SoftwareSleepSign for Animal KISSEI COMTEC Co., LTD. ( JAPAN )

  2. Algorithm for sleep scoring in experimental animals based on fast Fourier transform power spectrum analysis of the electroencephalogram Sayaka Kohtoh,1 Yujiro Taguchi,1 Masashi Wada,2 Naomi Matsumoto,2 Zhi-Li Huang,2 and Yoshihiro Urade2 1Department of Medical Systems, Kissei Comtec Co., Ltd., Nagano, and 2Department of Molecular Behavioral Biology, Osaka Bioscience Institute, Osaka, Japan Sleep Biological and Rhythms Volume 6 Number 3 July 2008 Accepted

  3. A top notch research institute dedicating to the sleep mechanism • Ranked as having one of the highest ratios of scientific Papers citing in the world • Discovered Prostaglandin D2 in 2002 • An introduction of OBI appeared in ASBMB (October 2006, P10~12 ) http://www.obi.or.jp/ Joint project withOsaka Bioscience Institute (OBI), Japan

  4. Advantages • We selected 4 parameters EEG δ power , EEG θ / (δ+θ) ratio , EMG integral , Activity • Percent agreement 90.9 ± 4.0%for rats ,90.0 ± 3.2%for mice • Processing speed is 1/6 of the existing version • Multiple animals are scored at once

  5. Activity EEG EMG Integral Fast Fourier Transform Scoring Logic Wake Non REM REM New Automatic Scoring Algorithm

  6. Active wake Quiet wake REM sleep NREM sleep EEG EMG LOC rat FFT spectrum EEG EMG mouse LOC FFT spectrum Typical waveform and FFT spectrum

  7. REM EEG q/(d+q) ratio (%) rat Non REM Wake NonREM REM REM Non REM mouse EEG q/(d+q) ratio (%) EEG d power (mV2) EMG integral (mV/sec) 3 parameters to separate NREM, REM, and Wakefulness

  8. EEG δ wave Locomotor Locomotor EEG θ wave Step : 1 Activity ? No Yes EEG EMG Step : 2 Active Wake High EEGdpower ? No Yes EEG / EMG Step : 3 High EEG θ ratio and High EMG Integral NREM Sleep Yes W R No NR REM Sleep Quiet Wake Hypnogram Scoring Logic

  9. rat Light phase Dark phase Locomotion activity (count) EEG d power (mV2) EEG q/(d+q) ratio(%) EMG integral (mV/sec) W R NR 12:00 14:00 16:00 24:00 02:00 04:00 Clock time Time courses of parameters and hypnogram

  10. mouse Light phase Dark phase Locomotion activity (count) EEG d power (mV2) EEG q/(d+q) ratio(%) EMG integral (mV/sec) W R NR 14:00 16:00 18:00 22:00 24:00 02:00 Clock time Time courses of parameters and hypnogram

  11. % % Percent agreement between FFT algorithm and visuals

  12. 250 4 hr 200 Processing time in pooled 24hr-data from 8rats(min) 1 / 6 150 1 / 80 100 50 40 min 3 min 0 Processing time

  13. SD Rat_1 Control SD Rat_2 SD Rat_1.csv SD Rat_2.csv SD Rat_3.csv SD Rat_4.csv SD Rat_3 SD Rat_4 After Scoring : Quick Report Making in Excel

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