The demographic and clinical variables of this study are given in Table 1. No significant differences were observed between groups for both age and gender. Similar overall global cognitive impairment was displayed between the dementia groups. However, the AD group displayed significant differences with the DLB and PDD groups in lower CAMCOG memory impairment and NPI hallucinations when utilising a Bonferroni correction (p < 0.05), which is displayed in supplementary Table 2. Additionally, it was found that AD patients displayed a significant difference in CAMCOG total score when compared to DLB patients which is not seen when comparing the AD and PDD patients, which can also be found in supplementary Table 2.
Table 1 Demographic and clinical variables for HC, AD, DLB and PDD groups, including descriptive statistics for each variable.
Importantly, there were no significant differences in the usage of cholinesterase inhibitors across dementia groups. The Bonferroni corrected values are displayed in supplementary Table 3.
Neighbourhood component analysis (NCA):
NCA was performed on all data sets to remove redundant features (that could lead to decreased classification accuracies for machine learning due to overfitting). Notably NCA transforms and maximises the performance of features for utilisation in k-nearest-neighbour . We performed these simulation 100 times to ascertain model consistency, and as can be seen in Figure 1a and 1b the overall model consistently chooses the same features for classification across multiple runs, with 4 features chosen in >95% of runs for HC-Dementia and 3 features for AD-DLB. For HC vs dementia the EC Frontal High Theta, EC Central Theta, EC Occipital Delta, and the ratio between the EC and EO dominant frequency variance (DFV) in the parietal region were chosen; for AD-DLB EC Frontal Beta, EO Frontal High Theta and EO Parietal High Theta; for AD-DLB/PDD the EC Temporal DFV, EO Frontal High Theta, EO Parietal High Theta, EO Occipital High Theta and EO Occipital DFV and finally for DLB-PDD classification only the EC Parietal Alpha and EO Occipital Delta features were chosen. Features were utilised for machine learning if they were picked in 95% of simulated runs, a full list of features tested for the HC-D group is displayed in supplementary Figure 1.
Dominant frequency, dominant frequency variance and theta alpha ratio.
From the one-way four group ANOVA it was found that there were significant differences for the eyes closed DF between the HC and dementia groups in the Parietal and Occipital regions, with dementia groups displaying a mean slowing in their DF towards the high-theta frequency range. Additionally, we found the same significant difference between the HC and dementia patients DF in the EO resting state within the same regions. These results being displayed in supplementary Table 4.
Similarly, we investigated the significance of DFV in the EC and EO state between groups. By computing the ratio between the two states we found that healthy controls displayed a significant decrease in variance in the EC compared to EO state. Notably, this change was found to be significant across all cortical regions for the healthy control groups when compared to dementia groups. For the AD, DLB and PDD groups we found no significant change in DFV between the EC and EO states. These results are displayed in supplementary Table 5. Additionally, we investigated the EEG data scroll of participants between the two states and found that HC participants displayed an apparent change in frequency that was not seen for dementia patients, with some exemplary examples shown in supplementary Figure 2.
The ratio of the theta and alpha relative power (TAR) also showed a significant difference between the HC and dementia groups within the occipital region in both the EC and EO resting state. The parietal region showed a significant difference between the HC and the dementia groups within the EC state. However, in the EO state only the DLB and PDD groups were found to have a significantly greater TAR than the HC groups.
Machine Learning Classification
We investigated inter-group group separability using machine learning classification for EC and EO spectral features. To this end, we only used features which had been chosen via feature selection. A k-Nearest-Neighbour model classifier was chosen for inter-group classification. A k value of 10 was selected for cross fold validation between different participant groups. A summary of the results for classification between healthy controls and dementia patients in addition to classification between the AD, DLB and PDD groups can be found in Table 2 for EC and EO qEEG features.
Table 3 summarises the classification results between the AD and DLB patient groups when utilising only EC qEEG features and combining EC and EO qEEG features, for the k-nearest neighbour algorithm, logistic regression, and a quadratic support vector machine.
Additionally, we investigated the change in classification accuracy for AD vs DLB classification when one includes clinical scores. With CAMCOG memory score being chosen during feature selection alongside EC Frontal beta and EO parietal high theta. With a marked increase in accuracy for identifying patients with DLB. These results being presented in Table 4.
Table 2: Comparison of classification results between HCs and Dementia patients as well as AD and DLB using 10-fold cross validation. Utilising a K-Nearest-Neighbour machine learning model, in addition we present the Confidence interval (CI) for the specificity and sensitivity of each classification type.
Table 3: AD – DLB Classification results comparison for EC and EC-EO classification using 10-fold cross validation, with comparisons across 3 separate machine learning models: k-nearest neighbour, logistic Regression, and support vector machine.
Table 4: Improved AD – DLB Classification results when including CAMCOG memory total score for classification, done for both EC + EO and EC only classification.