J Jing1, J Dauwels2, T Rakthanmanon3, E Keogh4, S S Cash5, M B Westover6. 1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: jingjin@ntu.edu.sg. 2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: justin@dauwels.com. 3. Department of Computer Engineering, Kasetsart University, Thailand. Electronic address: thanawin.r@ku.ac.th. 4. Department of Computer Science and Engineering, University of California, Riverside, CA, USA. Electronic address: eamonn@cs.ucr.edu. 5. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, MA, USA. Electronic address: scash@mgh.harvard.edu. 6. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, MA, USA. Electronic address: mwestover@mgh.harvard.edu.
Abstract
BACKGROUND: EEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation. NEW METHODS: At the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation. RESULTS: Our results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20-90%, with an average of approximately 70%. COMPARISON WITH EXISTING METHOD(S): In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence. CONCLUSION: NeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.
BACKGROUND: EEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation. NEW METHODS: At the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation. RESULTS: Our results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20-90%, with an average of approximately 70%. COMPARISON WITH EXISTING METHOD(S): In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence. CONCLUSION: NeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.
Authors: Jin Jing; Haoqi Sun; Jennifer A Kim; Aline Herlopian; Ioannis Karakis; Marcus Ng; Jonathan J Halford; Douglas Maus; Fonda Chan; Marjan Dolatshahi; Carlos Muniz; Catherine Chu; Valeria Sacca; Jay Pathmanathan; Wendong Ge; Justin Dauwels; Alice Lam; Andrew J Cole; Sydney S Cash; M Brandon Westover Journal: JAMA Neurol Date: 2020-01-01 Impact factor: 18.302
Authors: Jin Jing; Aline Herlopian; Ioannis Karakis; Marcus Ng; Jonathan J Halford; Alice Lam; Douglas Maus; Fonda Chan; Marjan Dolatshahi; Carlos F Muniz; Catherine Chu; Valeria Sacca; Jay Pathmanathan; WenDong Ge; Haoqi Sun; Justin Dauwels; Andrew J Cole; Daniel B Hoch; Sydney S Cash; M Brandon Westover Journal: JAMA Neurol Date: 2020-01-01 Impact factor: 18.302
Authors: Jiangling Song; Haoqi Sun; Jin Jing; Luis Carlos; Lingya Chao; Sydney S Cash; Rui Zhang; M Brandon Westover Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2018-07