Presented by: Aaron Raymond See Advisor: Prof. Shih-Chung Chen Investigating the Use of an Alternative Electrode Placement from C3-A2 to Fp1-A2 for Sleep EEG Analysis Presented by: Aaron Raymond See Advisor: Prof. Shih-Chung Chen 19.09.2018

Outline Introduction Materials and Methods Results Conclusion References 9/19/2018

Introduction Research on sleep—the most basic of natural phenomena—has become increasingly prevalent in the past decade mainly due to the rising number of people suffering from sleep disorders. J. Orzel-Gryglewska, Int. J. Occup. Med. Environ. Health, 23 (2010) 95-114. L.R.A. Bittencourt, R. Santos-Silva, M.T. De Mello, M.L. Andersen, S. Tufik, J. Occup. Rehabil., 20 (2010) 21-32. 9/19/2018

Introduction Traditional analytic methods for sleep study have used the polysomnograph However, this is not only uncomfortable for the patients and expensive for frequent diagnosis, but also time consuming for sleep technicians. 9/19/2018

Fig. 1 PSG Measurement Scenario 9/19/2018

Introduction Concerning automated sleep-stage studies, advancements in the use of innovative signal processing were introduced beginning in the year 2000. H. Park, Automated Sleep Stage Analysis Using Hybrid Rule-Based and Case-Based Reasoning, (2000) 144. C. Berthomier, X. Drouot, M. Herman-Stoica, P. Berthomier, J. Prado, D. Bokar-Thire, O. Benoit, J. Mattout, M.P. d'Ortho, Automatic analysis of single-channel sleep EEG: Validation in healthy individuals, Sleep, 30 (2007) 1587-1595. L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, H. Dickhaus, Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier, Comput. Meth. Programs Biomed., 108 (2012) 10-19. S.R.I. Gabran, S. Zhang, M.M.A. Salama, R.R. Mansour, C. George, Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes, Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 2008, pp. 1136-1139. Y.-L. Hsu, Y.-T. Yang, J.-S. Wang, C.-Y. Hsu, Automatic sleep stage recurrent neural classifier using energy features of EEG signals, Neurocomputing, 104 (2013) 105-114. 9/19/2018

Introduction Traditional EEG recordings, places electrodes on the C3-A2 and C4-A1 region. However, it is rather challenging for a person to properly place multiple physiological electrodes, or even a single EEG electrode, to measure their sleep quality in these areas A. Rechtschaffen, A. Kales, A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Allan Rechtschaffen and Anthony Kales, editors, U. S. National Institute of Neurological Diseases and Blindness, Neurological Information Network, Bethesda, Md., 1968. 9/19/2018

Fig. 2 Electrode placements FP1 FP2 C3 C4 Oz Fig. 2 Electrode placements 9/19/2018

Introduction van Sweden et al. (1990) have proposed the use of an alternate EEG placement from the traditional C3-A2 to Fpz-Cz. Findings showed significant agreement. However, there were instances wherein the alternative placement scored a deeper sleep stage compared to the original EEG placement. B. van Sweden, B. Kemp, H.A. Kamphuisen, E.A. Van der Velde, Alternative electrode placement in (automatic) sleep scoring (Fpz-Cz/Pz-Oz versus C4-A1), Sleep, 13 (1990) 279- 283. 9/19/2018

Introduction DLPFC exerted a critical role for executive control and in attention control, wherein borderline sleepiness may be manifested M. Izzetoglu, K. Izzetoglu, S. Bunce, H. Ayaz, A. Devaraj, B. Onaral, and K. Pourrezaei, (2005), “Functional near-infrared neuroimaging,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 13, No. 2, pp. 153-159, June 2005. T.S. Braver, and J.D. Cohen, “Working memory, cognitive control, and the prefrontal cortex: Computational and empirical studies,” Cognitive Processing, Vol. 2, pp. 25-55, 2001. S. Bunge, and J. Wallis, Neuroscience of Rule-Guided Behavior. Oxford: Oxford University Press, 2008. Low, K. A., Leaver, E. E., Kramer, A. F., Fabiani, M., & Gratton, G. (2009), “Share or compete? Load- dependent recruitment of prefrontal cortex during dual-task performance,” Psychophysiol, Vol. 46, No. 5, pp. 1069-1079, 2009. 9/19/2018

Introduction Therefore, the current study will investigate whether the Fp1-A2 and Fp2-A1 electrode placements are analogous to the C3-A2 and C4- A1 channel. 9/19/2018

Materials and methods Subjects 5 healthy (“normal”) volunteers recruited to undergo sleep assessment to determine the utility and efficacy of the alternate electrode placement. The subjects were all male with an average age of 23.8 ± 3.11 years old. 9/19/2018

Materials and methods Polysomnographic (PSG) midday sleep recordings were performed for 2 to 4 hours between 2P.M. and 6P.M. under standard recording conditions in a sleep laboratory. 9/19/2018

Materials and methods Data were recorded using the Somte PSG The following physiological signals were continuously recorded: EEG (C3-A2, C4-A2, Fp1-A2 and Fp2-A1) Horizontal electrooculogram (EOG) Chin electromyogram (EMG), heart rate, respiration (nasal/mouth flow), and oxygen saturation using an oximeter. Sampling rate for the EEG acquisition was 200Hz 9/19/2018