ParkinsonDream

mPower feature extraction for Parkinson's disease DREAM challenge

This repository contains code for automatically extracting features that are predictive of Parkinson's disease using deep learning. We validated our method in the Parkinson's disease digital biomarker DREAM challenge. For a summary of our approach please can be found here.

Requirements

To run the scripts you need the following software requirements:

Install Anaconda2-4.4.0.

Subsequently, the necessary requirements can be installed using conda create --name <env> --file requirements.txt

Environment variables

You need to set the environment variable PARKINSON_DREAM_DATA to point to the directory where the dataset should be stored. For instance, on Linux use

export PARKINSON_DREAM_DATA=/path/to/data/

Training

The individual models are pre-trained on the specified dataset according to

cd <repo_root>/code

# Pre-training for submission_v1.csv
python run_all.py -df svdrotout -mf conv3l_30_300_10_20_30_10_10 --rofl
python run_all.py -df flprotres -mf conv2l_30_300_10_20_30 --rofl
python run_all.py -df rrotret -mf conv2l_30_300_10_20_30 --rofl
python run_all.py -df fbpwcuaout -mf conv3l_30_300_10_40_30_10_10 --rofl
python run_all.py -df fbpwcuaout -mf conv2l_30_300_10_20_30 --rofl
python run_all.py -df svduaret -mf conv2l_30_300_10_20_30 --rofl

# Pre-training for submission_v2.csv
python run_all.py -df svdrotout -mf conv2l_30_300_10_40_30 --rofl
python run_all.py -df flprotres -mf conv2l_30_300_10_40_30 --rofl
python run_all.py -df rrotret -mf conv2l_30_300_10_20_30 --rofl
python run_all.py -df fhpwcuaout -mf conv3l_50_300_10_20_30_10_10 --rofl
python run_all.py -df fbpwcuaout -mf conv2l_50_300_10_40_30 --rofl
python run_all.py -df fhpwcuaout -mf conv2l_30_300_10_40_30 --rofl

These commands will also automatically download and preprocess the mPower.

For submission_v2.csv, an integration model was used that used the top-level feature activities from the pre-trained models as input for neural network consisting of two layers. The integraion model was trained using

python merge_classifier.py alldata.integration1

Feature prediction

Finally, the feature predictions were generated using

python featurizer.py --genfeat1 --genfeat2