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⛄ 内容介绍
在如今生活节奏快,工作竞争激烈的社会环境的影响下,抑郁症已成为人群中一种常见的精神疾病,并成为了全球性公共卫生问题.对抑郁症进行准确的诊断以及预测病情能否通过药物有效控制(不同类型细分),对于确定合适的诊疗方案具有重要的意义.但是,目前临床上对抑郁症的诊断方式主要是通过对患者进行量表问询.该方式下患者回答的真实性有待考量且受医生经验和水平等因素的影响,诊断结果较为主观且易发生误诊的情况.近年来,已有大量的研究从大脑医学影像的角度寻找抑郁症的生物学诊断指标
⛄ 部分代码
% @=============================================================================
% Reference: Identifying Neuroimaging Biomarkers of Major Depressive Disorder
% from Cortical Hemodynamic Responses
% Using Machine Learning Approaches
% =============================================================================@
%
function main()
clc; clear; close all;
% Add current path and all subfolders to the path.
addpath(genpath(pwd));
% -----------------------------------------------------------------
% Pre-processed NIRS signals by: linear fitting, moving average, and
% removing artifact channels.
% The generated ∆HbO dataset:
% samples_52ch_HbO.mat
% -----------------------------------------------------------------
% Three steps of fNRIS signals analysis for differing MDDs from HCs
step = 3;
if step == 1
Method1_RankingFeatures();
elseif step == 2
Method2_GASelection();
elseif step == 3
Validation();
end
end
%% ===================================================================
% Feature Selection Method I: Ranking Features by Statistical Test
%
function Method1_RankingFeatures()
% (1) A data matrix consists of 52 channels × 16 variables was extracted.
% (2) Statistical test was applied to find the significantly different
% channels on each variable, and subsequently generate feature channels
% as predictors for a classifier.
stat_result = statistics_test_feature();
% Results:
% Supplementary Figure 1 - Color Map
% Supplementary Figure 2 - count_sigdiff_channels
% Supplementary Table 3 - hc_cf_mstd, mdd_cf_mstd, pvalues_cluster
% % generate feature set with significant difference
% generate_sigdiff_feature(stat_result.feature_names, ...
% stat_result.diff_feature_cluster, ...
% stat_result.count_sigdiff_channels);
% (3) Five supervised models were implemented to learn pattern
% from feature channels
% (6)(10) Performances were evaluated by five-fold cross-validation and
% prediction accuracy
data_type = 'sigdiff_feature_topsigch'; % 'sigdiff_feature_topcluster'
feature_type = 'feature_channel';
feature_fname = 'featureset_sigdiff_5ch.mat';
integral_type = '';
centroid_type = '';
model_type = 'funcfit_nb';
[pred_train, pred_test] = test_feature_performance(data_type, ...
feature_type, feature_fname, ...
integral_type, centroid_type, ...
model_type);
% Result: Supplementary Table 6
% (10) performances were estimated by nested cross-validation
[result_inner_cv, result_outer_train, result_outer_test] = ...
nested_crossvalidation(data_type, ...
feature_type, feature_fname, ...
'', '', model_type);
% Result: Supplementary Table 6
end
%% ===================================================================
% Feature Selection Method II: Two-phase Feature Selection by Genetic Algorithm
%
function Method2_GASelection()
% -------------- Phase-One --------------
% The input is the candidate channels from one of the 10 significant variables,
% while the output is a channel subset of the specific variable.
% The optimization of channel selection was performed over all 10 variables.
data_type = 'integral';
feature_type = 'feature_channel';
feature_fname = '';
integral_type = 'integral_stim';
centroid_type = '';
model_type = 'funcfit_svm';
func_pop = @func_population_rand;
binary_ga(data_type, feature_type, feature_fname, ...
integral_type, centroid_type, model_type, func_pop);
% Result: ga_ma50_integral_stim__svm_0.7316_0.7363.mat, etc.
% -------------- Phase-Two --------------
% The selected channel subsets from 10 significant variables were then
% combined into a feature set, i.e., fusion features.
generate_fusion_feature('svm');
% Result: fusion_10variants_svm.mat, etc.
% GA learned which feature channels contributed best to the accuracy
% of a supervised model.
data_type = 'fusion_feature';
feature_type = 'feature_channel';
feature_fname = 'fusion_10variants_svm.mat';
model_type = 'funcfit_svm';
func_pop = @func_population_optm;
binary_ga(data_type, feature_type, feature_fname, ...
'', '', model_type, func_pop);
% Results:
% ga_fusion_features_svm_0.8053_0.7802.mat, etc.
% Supplementary Figure 3
end
%% ===================================================================
% Validate the Performance of Optimal Features
%
function Validation()
% -----------------------------------------------------------------
% Classification performances were reported by the
% 5-fold cross-validation in training set and
% prediction accuracy in test set.
data_type = 'ga_optimal_feature';
feature_type = 'feature_channel';
feature_fname = 'ga_fusion_features_svm_0.8053_0.7802.mat';
model_type = 'funcfit_svm';
[pred_train, pred_test] = test_feature_performance(data_type, ...
feature_type, feature_fname, ...
'', '', model_type);
% Result: TABLE 1 of main text
% ----------------------------------------------------------------------
% Classification performances were estimated by nested cross-validation
[result_inner_cv, result_outer_train, result_outer_test] = ...
nested_crossvalidation(data_type, ...
feature_type, feature_fname, ...
'', '', model_type);
% Result: TABLE 1 of main text
% -------------- Characteristics of optimal feature --------------
p1_fus_feature_fname = 'fusion_10variants_svm.mat';
p2_opt_feature_fname = 'ga_fusion_features_svm_0.8053_0.7802.mat';
[pvalues_optfeatures, pvalues_roifeatures, ...
hc_roi, mdd_roi, count_common_channels] = ...
test_optimalfeature(p1_fus_feature_fname, p2_opt_feature_fname);
% Results:
% Figure 2 of main text -- hc_roi, mdd_roi
% Figure 3 of main text -- count_common_channels
% Supplementary Table 4 -- hc_roi, mdd_roi, pvalues_roifeatures
% -------------- Common features between different models --------------
% test_common_features();
end
⛄ 运行结果
⛄ 参考文献
[1]江筱, 邵珠宏, 尚媛园,等. 基于级联深度神经网络的抑郁症识别[J]. 计算机应用与软件, 2019, 36(10):7.
