Depression diagnosis classification

Name: depression diagnosis

Category: clinical

Dataset: Mumtaz2018

Objective: Binary classification

Split: Leave-subjects-out

Usage

neuralbench eeg depression_diagnosis

Show config.yaml

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

data:
  study:
    source:
      name: Mumtaz2018Machine
    split:
      name: SklearnSplit
      split_by: subject
      valid_split_ratio: 0.2
      test_split_ratio: 0.2
      valid_random_state: 33
      test_random_state: 33
      stratify_by: diagnosis
  target:
    =replace=: true
    name: LabelEncoder
    event_types: Eeg
    event_field: diagnosis
    return_one_hot: true
    aggregation: trigger
  trigger_event_type: Eeg
  start: 0.0
  duration: 5.0
  stride: 5.0
  summary_columns: [diagnosis]
compute_class_weights: true
brain_model_output_size: &brain_model_output_size 2
trainer_config.monitor: val/bal_acc
trainer_config.mode: max
loss:
  name: CrossEntropyLoss
  kwargs:
    label_smoothing: 0.1
metrics: !!python/object/apply:neuralbench.defaults.metrics.get_classification_metric_configs
  - *brain_model_output_size
test_full_metrics:
  - log_name: acc
    name: Accuracy
    kwargs:
      task: multiclass
      num_classes: *brain_model_output_size
  - log_name: bal_acc
    name: Accuracy
    kwargs:
      task: multiclass
      num_classes: *brain_model_output_size
      average: macro
  - log_name: auroc
    name: AUROC
    kwargs:
      task: multiclass
      num_classes: *brain_model_output_size

Description

This task consists in predicting whether subjects have received a major depressive disorder (MDD) diagnosis, or are healthy, based on their EEG recordings in eyes closed, eyes open, and P300 task conditions [Mumtaz2018].

References

[Mumtaz2018]

Mumtaz, Wajid, et al. “A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).” Medical & biological engineering & computing 56 (2018): 233-246.