SPZ : A Semantic Perturbation-based Data Augmentation Method with Zonal-Mixing for A lzheimer’s Disease Detection (2024)

Abstract: Alzheimer’s Disease (AD), characterized by significant cognitive and functional impairment, necessitates the development of early detection techniques. Traditional diagnostic practices, such as cognitive assessments and biomarker analysis, are often invasive and costly. Deep learning-based approaches for non-invasive AD detection have been explored in recent studies, but the lack of accessible data hinders further improvements in detection performance. To address these challenges, we propose a novel semantic perturbation-based data augmentation method that essentially differs from existing techniques, which primarily rely on explicit data engineering. Our approach generates controlled semantic perturbations to enhance textual representations, aiding the model in identifying AD-specific linguistic patterns, particularly in scenarios with limited data availability. It learns contextual information and dynamically adjusts the perturbation degree for different linguistic features. This enhances the model’s sensitivity to AD-specific linguistic features and its robustness against natural language noise. Experimental results on the ADReSS challenge dataset demonstrate that our approach outperforms other strong and competitive deep learning methods.

## introduction alzheimer's disease (ad) casts a shadow over the globe as an affliction of the mind, eroding cognitive and functional capabilities with devastating thoroughness @xcite . as the condition progresses, the irrevocable nature of this disorder becomes markedly pronounced, underscoring the imperative for early detection and opportune intervention. contemporary medical diagnostics have predominantly hinged on cognitive testing and bio-marker analysis (guo * corresponding author et al., 2020), yet these approaches are notably timeintensive and financially burdensome, thereby hindering their widespread adoption for potential patients. recent advancements in automated speech and text analysis offer a more cost-effective and scalable alternative for ad detection @xcite . patients with ad exhibit distinctive linguistic patterns @xcite , characterized by an increased reliance on filler words, a paucity of informative nouns and verbs, disorganized syntax, alongside heightened frequencies of pauses and hesitations @xcite . these linguistic features provide promising potential for language-based analysis mechanisms in detecting ad, indicating that enhanced sensitivity in capturing these patient-specific features could improve the efficiency of early-stage detection and intervention. with the advancements of deep learning, many neural network-based methods @xcite @xcite have been proposed to detect ad. yet, the primary challenges obstructing the development of effective detection lie in the limited sensitivity of existing methods to the unique linguistic features of ad patients and the shortage of labeled data. the difficulty in data collection @xcite , the high costs associated with data labeling, and privacy concerns have all hindered the availability of accessible and labeled linguistic datasets. this scarcity increases the risk of model overfitting and impedes the exploration of specific features that are highly relevant to ad patients. in recent years, data augmentation (da) has been proposed as an effective technique to alleviate the scarcity of available datasets and improve model generalization. previous da methods for ad detection @xcite @xcite primarily involve performing explicit transformations of the original text sequences (e.g., random deletion or lexical substi-tution etc.) @xcite @xcite , paraphrasing @xcite , or text generation @xcite . although these methods alleviate the problem of data availability to a certain extent, they might result in augmentation bias and impair the semantics of the original texts. first, the randomly deleted ad samples may contain ad-specific features removed, which are indeed semantically closer to non-ad samples. second, random operations have difficulty in preserving ad-specific features that are crucial for ad diagnosis. as a consequence, the model might be misled to learn inappropriate pseudo ad patterns, thereby hurting the model's generalization ability. in addition, various types of da methods require varying degrees of explicit data engineering, thus limiting the portability and applicability of these methods. to address the above issues, we aim to develop an effective data augmentation (da) method to improve the performance of ad detection in datalimited scenarios. our approach centers on utilizing semantic perturbations to enhance the sensitivity of ad detection models in capturing adspecific features while retaining the robustness to natural noise that may appear in user-generated texts. in our work, we propose a semantic perturbation with zonal-mixing (spz) da framework. spz consists of a generator module and a mediator module. the generator automatically augments the given text with varying degrees of noise generated from a gaussian distribution, optimized using a temperature-dependent gumbel-softmax trick @xcite . unlike generative adversarial networks (gans) that typically create new data samples, spz focus on adding controlled perturbations to the existing data samples. to control the degree to which different types of words are perturbed, the mediator assesses all initial word representations, assigning corresponding perturbation probabilities. finally, all initial word representations and generative noise representations are mixed to produce a refined representation as input to the final ad classifier. this dual-module approach ensures the preservation of the text's original semantics with subtle modifications to improve the effectiveness of model training. we illustrate that our proposed approach achieves competitive performance on the adress challenge dataset compared to strong baselines. our contributions are summarized as follows: ## the adress challenge dataset we utilize the adress dataset @xcite , which serves as a standardized benchmark for the alzheimer's disease (ad) research community, facilitating research in ad detection. this dataset comprises 156 speech samples with corresponding transcripts from participants-both non-ad (35 male, 43 female) and ad-affected (35 male, 43 female) english speakers-performing the cookie theft picture description task @xcite . our study focuses exclusively on the transcript data, formatted according to the chat protocol @xcite , a recognized standard for talkbank data. as shown in the adress dataset's limited size, resulting from data collection difficulties and privacy concerns, poses a significant challenge for developing robust ad classification models. to tackle this challenge, we propose a novel data augmentation method aimed at enhancing textual representations in data-limited scenarios. our strategy involves generating controlled semantic perturbations, de-gets noised with prob of and retains with prob of - ## methods our proposed method aims to generate better representations of alzheimer's disease (ad) transcripts in data-limited scenarios, which can be easily optimized and utilized to perform effective early-stage ad detection. in our framework, each transcript is initially embedded into a pre-trained language model bert @xcite at the token level, thereby obtaining representations for each word. these word representations are subsequently channeled through three distinct information pathways: 1) fed into a generator module, which is designed to derive semantic perturbations; 2) transmitted into a mediator module, which controls the corresponding degrees of perturbation for each word; 3) combined with the generative word semantic perturbations and then input into the final ad classifier. details of our proposed approach are presented in subsequent subsections. ## experiments we implement our proposed method using bert4keras (su, 2020) on nvidia tesla v100s with 32gb ram. accuracy is used as the primary performance metric due to the well-balanced nature of the adress challenge dataset. additionally, precision, recall and f1 scores with respect to the ad class are considered to offer a comprehensive evaluation. we use 10-fold cross-validation on the training dataset to assess generalization error, and report the average evaluation metrics across 5 different seeds. the learning rate is set to 2e -05 and batch size is set to 8. ## discussion and conclusion in this paper, we propose a simple yet effective data augmentation framework (spz) for alzheimer's disease detection under data-limited scenarios. spz comprises two key components: the generator module that generates token-level perturbations following skewed distributions, and the mediator module that controls the perturbation degree for each word. the augmented textual representations are ultimately derived via zonal-mixing, enabling accurate ad detection. our spz method fundamentally differs from gan-based da methods that primarily focus on generating entirely new samples through training generative networks and often require large amounts of training data. in contrast, our spz method applies controlled semantic perturbations to existing text samples, with the goal of enhancing textual representations for ad detection. this approach preserves the linguistic integrity of the original data while enhancing the model's sensitivity to ad-specific linguistic patterns. through generating controlled perturbations, spz offers the advantage of pinpointing subtle linguistic features associated with ad, thus yielding superior ad detection performance especially under data-limited settings. experimental results and detailed analyses demonstrate the potential of introducing semantic perturbations into textual representations to improve the accuracy of ad detection. compared to a series of strong and competitive baselines, our spz method with controlled perturbations achieves the best results on the adress challenge dataset. additionally, the findings in extended analysis provide a basis for further exploring explicit feature inference and investigating credible ad detection.