Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer’s Disease Diagnosis

Algorithm 1

Summary of methodology steps.

	Step 1: Feature extraction
(1)	Compute modulation spectrum from preprocessed EEG ⟶ Obtain X (f, f_mod) (equation (1))
(2)	Train CNN on five different classification tasks using training and validation partitions of available datasets
(a)	N vs. AD1 vs. AD2 (multiclass discrimination) ⟶ CNNa
(b)	N vs. AD (AD diagnosis) ⟶ CNNb
(c)	N vs. AD1 (early AD detection) ⟶ CNNc
(d)	AD1 vs. AD2 (AD progression) ⟶ CNNd
(e)	N vs. AD2 (late-stage AD detection) ⟶ CNNe
(3)	Find saliency map of each CNNi (i = a, , e) using validation partition data
(4)	Cluster saliency islands using the k-means algorithm into final “patches”
	Step 2: Train/testing with feature selection
(1)	Compute modulation energy from patches from all electrodes for each CNNi
(2)	Apply feature selection based on ANOVA for each CNNi
(3)	Run leave-one-subject-out cross-validation using unseen test set data with a SVM classifier for each CNNi
(4)	Calculate classifier figures of merit for CNNi