Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model’s decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition; then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD.

An adequate number of hidden neurons and connection structure of a multi-layer perceptron network (MLP) are usually determined by experimentation. In this paper, we propose a scheme to define an appropriate structure and number of neurons of a partially connected MLP when used for classification. Rules for designing the network are based on a decision tree previously built using information gain. Our structure, called IG Net, is inspired by the Entropy Net [1], but contains fewer layers and connections than such network or than a fully-connected neural network and holds equivalent classification power. We tested the classification performance of our network using 10 databases from the UCI Machine Learning Repository. The performance obtained by IG Net using such databases showed to be statistically equivalent to the one obtained by an Entropy Net or by a fully-connected MLP, using fewer computational resources than the compared models.