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A Scalable Automated Diagnostic Feature Extraction System for EEGs

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A Scalable Automated Diagnostic Feature Extraction System for EEGs

A group project by: Gokul Krishna, Xiao Han, Divya bhargavi, Prakhar Agrawal, Neha Tevathia

Abstract:

Researchers using Electroencephalograms (“EEGs”) in order to diagnose clinical outcomes often run into computational complexity problems. In particular, extracting complex, sometimes non-linear, features from a large number of time series can take large amounts of time. In this project we create a system which uses cloud-based technologies to demonstrate that such tools can increase the efficiency of computation.

Pipeline:

you can read more about the pipeline and how to set up in my blog post here

Pipeline

Tools:

Python, PySpark, MongoDB, AWS S3, AWS EMR


This repository is the duplication of the original private repository with confidential contents removed.

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