other · King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) · 2019

Semi-automative binary classification workflow

Savenko, Oksana, Li, Haoyang

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Abstract

SEMI-AUTOMATED BINARY CLASSIFICATION WORKFLOW\n\nMOTIVATION\n\nNowadays, there is a recent call to build a human-independent intelligence which can assist clinicians during medical diagnosis. In our research, we focus on development of a standartized and tunable workflow for the binary classification problem whithin variuos biological signals (EEG, fMRI, NIRS). This workflow can be openly used by others in their own studies, research and clinical practice.\n\nOBJECTIVES\n\n1. Evaluate efficiency of different classification methods on well- known in community datasets\n\n2. Merge produced code into separate workflows for datasets depending on their dimentionality (single-channel, multi- channel, volumetric)\n\nCHALLENGES\n\n1. Absence of default data formating among researchers which requires human intervention.\n\n2. Optimization of the code architecture and workflow parallelization (choosing among a range of preprocessing steps, feature generation and classification methods.\n\nMETHODS\n\nIn the central part of the poster one may see a picture of two best performing on fMRI Star-Plus dataset workflows we have inside our generalized workflow for multi-dimensional data. Below we describe other options we provide the user whithin the project.\n\nPREPROCESSING\n\nFrequency filtering\n\nIndependent Component Analysis\n\nSmoothing (exponentional, rollong mean)\n\nGlobal Signal Regression\n\nFEATURE GENERATION\n\nFast Fourier Transform\n\nSemi-Classical Signal Analysis\n\nUTILIZED CLASSIFIERS\n\nSupport Vector Classifier\n\nLogistic Regression\n\nDecision Tree Classifier\n\nK-Nearest Neighbours\n\nNeural Network\n\nConvolutional Neural Network\n\nCONCLUSIONS\n\nWe tried to include state of the art practics of brain data analysis and include some novel methods like SCSA. Currently the project is still under development but if you findit interesting and prospectively useful for yourself we suggest to pass our tutorial on single-channel data analysis which is a part of the project and explains the methods behind the workflow.

Cite this paper

APA 7

Savenko, O., & Li, H. (2019). Semi-automative binary classification workflow. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology).

MLA 9

Savenko, Oksana, and Haoyang Li. “Semi-automative binary classification workflow.” King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology), 2019.

Chicago

Savenko, Oksana, and Haoyang Li. 2019. “Semi-automative binary classification workflow.” King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology).

Harvard

Savenko, O. and Li, H. (2019) ‘Semi-automative binary classification workflow’, King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology).

BibTeX

@misc{savenko2019semi,
  title = {{Semi-automative binary classification workflow}},
  author = {Savenko, Oksana and Li, Haoyang},
  journal = {King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)},
  year = {2019}
}

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