config.yaml

---
debug: True

preprocess:
  root_path: '/ssd/ssd0/data/'
  idx_path: 'index/'
  waveform_path: 'waveform/'
  save_path: 'preprocessed/'    # Path for saving preprocessed data
  mimic:
    clinical_path: 'MIMIC/Clinical/mimic-iv-2.2/'
    ecg_path: 'MIMIC/ECG/mimic-iv-ecg-diagnostic-electrocardiogram-matched-subset-1.0/'
  flag: False                           # If True, preprocess will be executed before training
  all: True                             # If True, all data will be preprocessed
  multi: False                          # If debugging with Pycharm,
                                        # goto "Settings"->"Debugger"->"Python Debugger"->Set Gevent Compatible True
  data:                                 # configs for splitting data for preprocessing
    internal: False                     # If True, internal data will be preprocessed
    train: False                        # If True, train data will be preprocessed (only for internal data)
    af: True                            # If True, AFIB_OR_AFL data will be selected
    time: 10                            # Length of time for each data, used for checking preprocessed data length

  option:
    augment: True                       # If True, data augmentation will be executed
                                        # (only for AFIB_OR_AFL data, by four times using different preprocessing steps)
    target_fs: 125                      # Target sampling rate for resampling
    detrend: baseline                   # baseline for ECG, total for PPG and ABP
    denoise: soft                       # soft, hard
    normalize: minmax                   # minmax, zscore

train:
  model_save_path: 'models/'            # Path for saving model

  general:
    load_augmented: True                # If True, augmented data will be loaded for training sequence
    split_ratio: 0.8                    # Ratio for train and validation split
    number_of_classes: 2                # Number of classes for classification
    epochs: 200
    early_stop_n: 10                    # Number of epochs for early stopping
    loss_fn: BCEWithLogitsLoss          # BCEWithLogitsLoss
    batch_size: 512

  hyperparameter:
    in_channels: 1                      # Number of channels for input data
    out_channels: 32                    # Number of channels for first convolution layer
    dropout: 0.25
    lr: 0.001
    weight_decay: 0.1
    gamma: 0.99

test:
  batch_size: 512
  metrics: ['AUROC', 'Sensitivity', 'Specificity', 'Accuracy', 'Precision', 'Recall', 'F1-score']
  threshold: 0.5                        # Threshold for binary classification
  save_path: 'results/'

wandb:
  flag: False                           # If True, log will be sent to wandb
  project: ECG
  entity: paperchae