Who needs context? Classical techniques for A lzheimer’s disease detection (2023)

Abstract: Natural language processing (NLP) has shown great potential for Alzheimer’s disease (AD) detection, particularly due to the adverse effect of AD on spontaneous speech. The current body of literature has directed attention toward context-based models, especially Bidirectional Encoder Representations from Transformers (BERTs), owing to their exceptional abilities to integrate contextual information in a wide range of NLP tasks. This comes at the cost of added model opacity and computational requirements. Taking this into consideration, we propose a Word2Vec-based model for AD detection in 108 age- and sex-matched participants who were asked to describe the Cookie Theft picture. We also investigate the effectiveness of our model by fine-tuning BERT-based sequence classification models, as well as incorporating linguistic features. Our results demonstrate that our lightweight and easy-to-implement model outperforms some of the state-of-the-art models available in the literature, as well as BERT models.

## introduction alzheimer's disease (ad) is the most prevalent form of dementia, a neurodegenerative disease that impairs cognitive functioning and is increasingly common in our aging society @xcite . according to the world health organization, approximately 55 million people currently suffer from dementia, with this number expected to surge to 78 million and 139 million by 2030 and 2050, respectively @xcite . symptoms of ad include (but are not limited to) memory decline, disorientation, confusion, and behavioural changes. importantly, ad progression can lead to loss of independence which significantly impacts patients, their families, and society as a whole @xcite . given that late-stage ad progression is inevitable, early detection of ad through cost-effective and scal-able technologies is critical. while most clinical diagnoses of ad rely on neuroimaging, there is a critical need for more accessible and efficient methods of diagnosis. accessible evaluation methods for ad include cognitive tests such as the mini-mental status examination (mmse) @xcite and the montréal cognitive assessment (moca) @xcite . however, these methods still require active integration with an expert, and their specificity in early-stage diagnosis is questionable. during the course of ad, patients experience a gradual deterioration of cognitive function and accordingly may face a loss of lexical-semantic skills, including anomia, reduced word comprehension, object naming problems, semantic paraphasia, and a reduction in vocabulary and verbal fluency @xcite @xcite . speech processing and, consequently, natural language processing (nlp) can therefore provide new precision medicine tools for ad diagnosis that deliver objective quantitative analyses and reliable proof, analysis, comparison, and circulation for faster diagnosis. the alzheimer's dementia recognition through spontaneous speech (adress) challenge of in-terspeech 2020 is a shared database developed to advance research into automatic ad detection based on spontaneous speech and transcripts @xcite . participants in the challenge were tasked with describing the cookie theft picture in english, which is part of the boston diagnostic aphasia exam @xcite . the first set of the adress 2020 database comprises speech recordings and clan-annotated transcripts of 54 ad patients and 54 sex-and age-matched controls. various groups have worked with the adress dataset, approaching the problem from different perspectives and leveraging available information. these studies typically combined speech processing and linguistic feature extraction or nlp-based fine-tuning. the literature on speech processing mostly focused on zero-crossing rate, spectral bandwidth, roll-off, and centroids of audio recordings, as well as active data representation cluster-based feature extraction methods including the emobase @xcite , compare @xcite , and multi-resolution cochleagram (mrcg) @xcite feature sets. meanwhile, linguistic features have extracted lexical richness, the proportion of various pos tags, utterance duration, total utterances, type-token ratio, openclosed class word ratio, and similarity between consecutive utterances. nlp-based methods have comprised from-scratch training or fine-tuning contextbased models, such as bidirectional long shortterm memory (bi-lstm) @xcite , bi-directional hierarchical attention network (bi-hann) @xcite , convolutional recurrent neural network (crnn) @xcite , and bidirectional encoder representations from transformer (bert) @xcite . despite excellent performance compared to baseline methods @xcite , the complexity of these methodologies and the need to implement them on high-memory gpus highlights the need to explore simpler methodologies that can ensure ease and performance in ad detection. in this paper, we present a novel approach for detecting ad in the first set of adress dataset by integrating a new word2vec-based model and dimension reduction method. we not only implement and compare top-cited and recent state-of-the-art models on the same dataset, but also demonstrate that our approach outperforms these models. our proposed approach is simple, easy to implement, and highly accurate. ## discussion by mapping each word into a 500-dimensional space where words with similar context are closer together, the proposed model can identify when all words in a transcript are focused on the same topic with minimal deviations. coupled with the suggested standardization method, the results demonstrate a significant difference in performance between the proposed model and the only linguistbased model, which prioritizes utterances, pauses, and interactions between text and speech. the bert models explored in this study are relatively massive and require significant computational resources, and training them requires delicate hyperparameter optimization. in this study, we followed the bert authors' recommendations to keep the model's trainability on an nvidia rtx 3080 gpu and to avoid changing the weights of the model by selecting smaller epoch numbers. ## conclusions in this study, we introduced a word2vec-based model that combines pre-trained wikipedia embeddings with linguistic features. we also employed correlation-based feature selection to reduce the dimensionality of the embeddings. the results demonstrated that our proposed model outperformed existing models on the same dataset. however, as bert models offer diverse applicability, a potential future direction is to incorporate feature maps extracted from the hidden states of these networks to enhance the performance of our model.