Automatic diagnosis of Alzheimer's disease using neural network language models

In today's aging society, the number of neurodegenerative diseases such as Alzheimer's disease (AD) increases. Reliable tools for automatic early screening as well as monitoring of AD patients are necessary. For that, semantic deficits have been shown to be useful indicators. We present a way to significantly improve the method introduced by Wankerl et al. [1]. The purely statistical approach of n-gram language models (LMs) is enhanced by using the rwthlm toolkit to create neural network language models (NNLMs) with Long Short Term-Memory (LSTM) cells. The prediction is solely based on evaluating the perplexity of transliterations of descriptions of the Cookie Theft picture from DementiaBank's Pitt Corpus. Each transliteration is evaluated on LMs of both control and Alzheimer speakers in a leave-one-speaker-out cross-validation scheme. The resulting perplexity values reveal enough discrepancy to classify patients on just those two values with an accuracy of 85.6% at equal-error-rate