Joshua Levitt1, Adam Nitenson2, Suguru Koyama3, Lonne Heijmans1, James Curry4, Jason T Ross4, Steven Kamerling4, Carl Y Saab5. 1. Department of Neurosurgery, Rhode Island Hospital, and Department of Neuroscience, Brown University, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA. 2. Department of Neuroscience, Brown University, Providence, RI, USA. 3. Department of Neurosurgery, Rhode Island Hospital, and Department of Neuroscience, Brown University, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Laboratory for Pharmacology, Asahi KASEI Pharma Corporation, Shizuoka, Japan. 4. Global Therapeutics Research, Zoetis, Inc, Kalamazoo, MI, USA. 5. Department of Neurosurgery, Rhode Island Hospital, and Department of Neuroscience, Brown University, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA. Electronic address: Carl_Saab@Brown.edu.
Abstract
BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. NEW METHOD: We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. RESULTS: The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. COMPARISON WITH EXISTING METHODS: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. CONCLUSIONS: We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.
BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. NEW METHOD: We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. RESULTS: The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. COMPARISON WITH EXISTING METHODS: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. CONCLUSIONS: We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.
Authors: Suguru Koyama; Brian W LeBlanc; Kelsey A Smith; Catherine Roach; Joshua Levitt; Muhammad M Edhi; Mai Michishita; Takayuki Komatsu; Okishi Mashita; Aki Tanikawa; Satoru Yoshikawa; Carl Y Saab Journal: Sci Rep Date: 2018-11-06 Impact factor: 4.379
Authors: Muhammad M Edhi; Lonne Heijmans; Kevin N Vanent; Kiernan Bloye; Amanda Baanante; Ki-Soo Jeong; Jason Leung; Changfang Zhu; Rosana Esteller; Carl Y Saab Journal: Sci Rep Date: 2020-11-23 Impact factor: 4.379
Authors: Joshua Levitt; Muhammad M Edhi; Ryan V Thorpe; Jason W Leung; Mai Michishita; Suguru Koyama; Satoru Yoshikawa; Keith A Scarfo; Alexios G Carayannopoulos; Wendy Gu; Kyle H Srivastava; Bryan A Clark; Rosana Esteller; David A Borton; Stephanie R Jones; Carl Y Saab Journal: Neuroimage Date: 2020-08-29 Impact factor: 6.556