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    Semi-automative binary classification workflow

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    Type
    Poster
    Authors
    Savenko, Oksana
    Chahid, Abderrazak cc
    Laleg-Kirati, Taous-Meriem cc
    Date
    2020-1-20
    Permanent link to this record
    http://hdl.handle.net/10754/661208
    
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    Abstract
    SEMI-AUTOMATED BINARY CLASSIFICATION WORKFLOW MOTIVATION Nowadays, 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. OBJECTIVES 1. Evaluate efficiency of different classification methods on well- known in community datasets 2. Merge produced code into separate workflows for datasets depending on their dimentionality (single-channel, multi- channel, volumetric) CHALLENGES 1. Absence of default data formating among researchers which requires human intervention. 2. Optimization of the code architecture and workflow parallelization (choosing among a range of preprocessing steps, feature generation and classification methods. METHODS In 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. PREPROCESSING Frequency filtering Independent Component Analysis Smoothing (exponentional, rollong mean) Global Signal Regression FEATURE GENERATION Fast Fourier Transform Semi-Classical Signal Analysis UTILIZED CLASSIFIERS Support Vector Classifier Logistic Regression Decision Tree Classifier K-Nearest Neighbours Neural Network Convolutional Neural Network CONCLUSIONS We 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.
    Conference/Event name
    Digital Health 2020
    Additional Links
    https://epostersonline.com//dh2020/node/55
    Collections
    Digital Health 2020; Posters

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