Detecting Depression in Alzheimer and MCI Using Artificial Neural Networks (ANN)

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Depression is very common among patients with Alzheimer's while identifying depression in patients with Alzheimer's can be difficult, since dementia can cause some of the same symptoms. The related work in deep learning and machine learning proposed classification models that assist in detecting depression. However, classifying Alzheimer patients into depressive and non-depressive is not an easy task. Therefore, the objective of this research paper is to establish a starting point to use Artificial Neural Networks (ANN) to classify Alzheimer patients into depressive and non-depressive using speech analysis. The research paper proposes an analysis of the performance rates (accuracy, recall, precision) for ANN. The analysis performs three experiments and compare the performance rates among selected audio features. Our classification model shows promising classification results: the classification accuracy is ranged between 72.5% and 77.1%. This result provides a positive indication that ANN can assist the medical communities in future research. This could be accomplished by developing the feature extraction process, choosing the appropriate data and audio features, and developing the classification methods.