Detection of Apathy in Alzheimer Patients by Analysing Visual Scanning Behaviour with RNNs
Jonathan Chung (University of Toronto); Sarah A. Chau (University of Toronto); Nathan Herrmann (University of Toronto); Krista L. Lanctôt (University of Toronto); Moshe Eizenman (University of Toronto)
Assessment of apathy in patients with Alzheimer’s disease (AD) relies heavily on interviews with caregivers and patients, which can be ambiguous and time consuming. More precise and objective methods of evaluation can better inform treatment decisions. In this study, visual scanning behaviours (VSBs) on emotional and non-emotional stimuli were used to detect apathy in patients with AD. Forty-eight AD patients participated in the study. Sixteen of the patients were apathetic. Patients looked at 48 slides with non-emotional images and 32 slides with emotional images. We described two methods that use recurrent neural networks (RNNs) to learn differences between the VSBs of apathetic and non-apathetic AD patients. Method 1 uses two separate RNNs to learn group differences between visual scanning sequences on emotional and non-emotional stimuli. The outputs of the RNNs are then combined and used by a logistic regression classifier to characterise patients as either apathetic or non-apathetic. Method 1 achieved an AUC gain of 0.074 compared to a previously presented handcrafted feature method of detecting emotional blunting (AUC handcrafted = 0.646). Method 2 assumes that each individual’s “style of scanning” (stereotypical eye movements) is independent of the content of the visual stimuli and uses the “style of scanning” to normalise the individual’s VSBs on emotional and non-emotional stimuli. Method 2 uses RNNs in a sequence-to-sequence configuration to learn the individual’s “style of scanning”. The trained model is then used to create vector representations that contain information on the individual’s “style of scanning” (content independent) and her/his VSBs (content dependent) on emotional and non-emotional stimuli. The distance between these vector representations is used by a logistic regression classifier to characterise patients as either apathetic or non-apathetic. Using Method 2 the AUC of the classifier improved to 0.814. The results presented suggest that using RNNs to analyse differences between VSBs on emotional and non-emotional stimuli (a measure of emotional blunting) can improve objective detection of apathy in individual patients with AD.