Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification |
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| Institution: | 1. Advanced Information & Communication Technology Center (AICTC), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;2. Department of Biomedical Engineering, AmirKabir University of Technology, Tehran, Iran;3. Department of Electronic Systems Engineering, University of Essex, UK;1. Department of Computer Science and Software Science, Concordia University, Department of Computer Science, Montreal, QC, Canada H3G 1M8;2. Department of Computer Science, University of California, Davis, CA 95616, USA;1. Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, Hubei, China;2. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, Hubei, China;3. Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan;4. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK;5. Department of Mechanical Engineering, and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan;1. Department of Anesthesiology, Nantan General Hospital, Japan;2. Department of Anesthesiology, Kyoto Prefectural University of Medicine, Graduate School of Medical Science, Kyoto, Japan;1. School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China;2. Department of Mechanical Engineering, the University of Auckland, Auckland, New Zealand;3. State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan, China;1. Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia;2. DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France;3. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;4. Higher Institute of Industrial Systems of Gabes (ISSIG), University of Gabes, Gabes, Tunisia;5. Psychology Department, University of Montreal, QC, Canada |
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| Abstract: | The depth of anesthesia estimation has been of great interest in recent decades. In this paper, we present a new methodology to quantify the levels of consciousness. Our algorithm takes advantage of the fractal and self-similarity properties of the electroencephalogram (EEG) signal. We have studied the effect of anesthetic agents on the rate of the signal fluctuations. By translating these fluctuations with detrended fluctuation analysis (DFA) algorithm to fractal exponent, we could describe the dynamics of brain during anesthesia. We found the optimum fractal-scaling exponent by selecting the best domain of box sizes, which have meaningful changes with different depth of anesthesia.Experimental results confirm that the optimal fractal-scaling exponent on the raw EEG data can clearly discriminate between awake to moderate and deep anesthesia levels and have robust relation with the well-known depth of anesthesia index (BIS). When the patient's cerebral states change from awake to moderate and deep anesthesia, the fractal-scaling exponent increases from 0.8 to 2 approximately. Moreover, our new algorithm significantly reduces computational complexity and produces faster reaction to transients in patients’ consciousness levels compared to other algorithms and technologies. |
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