sparsh2706 / Parkinson-disease-detection-and-severity-prediction-from-gait

Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait

Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait

This is the official code release for the paper : El Maachi, I., Bilodeau, G.-A., Bouachir, W., Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait, Expert Systems With Applications, Volume 143, 2020

You can access it via arXiv: https://arxiv.org/abs/1910.11509

For any questions or queries, please contact Imanne El Maachi: [e-mail]

Prerequisites

• Python 3.7
• CPU or NVIDIA GPU + CUDA CuDNN (the algorithm was developed with Cuda and CudNN)
• Install the prerequisite's libraries: pip install -r requirements.txt

Dataset

The dataset used in the paper is from Physionet. It can be downloaded from: https://physionet.org/content/gaitpdb/1.0.0

A sample of the dataset is available in the subfolder data.

Getting Started

The entry point is train.py file. It has 3 arguments:

• input_data: Input folder containing the dataset.
• exp_name: type of experiment to run:
  • 'train_classifier': Run the cross-validation experiment for Parkinson detection.
  • 'train_severity': Run the cross-validation experiment for Parkinson severity prediction using the UPDRS scale.
  • 'ablation': Ablation study for the different gait signals for Parkinson detection.

Once executed, the algorithm will generate the following output files:

---

├── output (dir)
    ├── exp_name_Day_Month (when the program is launched) 
        ├── hour_minutes (when the program is launched)  
            ├──  weights.hdf5 : weights of the model
            ├──  res_seg.csv: results of accuracy, sensitivity and specificity by segments.
            ├──  res_pat.csv: results of accuracy, sensitivity and specificity by patients. 
            ├──  training_i.csv: training loss and accuracy for each fold i. For 10-fold cross-validation, 10 files. 
            ├──  model.json: Model architecture (keras)
            ├──  gt.csv: and pred.csv  ( for severity prediction) ground truth level and prediction level for each patient.
            ├──  confusion_matrix.csv: Confusion matrix for severity prediction