Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.

Ning Liu,Zhenming Yuan,Qingfeng Tang

doi:10.3389/fpubh.2021.835960

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criteria. A recent study has indicated the potential of language analysis for AD diagnosis. In this study, we proposed a novel feature purification network that can improve the representation learning of transformer model further. Though transformer has made great progress in generating discriminative features because of its long-distance reasoning ability, there is still room for improvement. There exist many common features that are not indicative of any specific class, and we rule out the influence of common features from traditional features extracted by transformer encoder and can get more discriminative features for classification. We apply this method to improve transformer's performance on three public dementia datasets and get improved classification results markedly. Specifically, the method on Pitt datasets gets state-of-the-art (SOTA) result.

Highlights

Alzheimer’s disease (AD) is a nervous degenerative disease with an insidious and irreversible onset, which is difficult to be detected in every stage
The dataset was divided into five parts randomly, of which four parts were used for training, and one part was used for test
Why the performance is better after purification? We know that transformer is superior to RNN, CNN on its longdistance reasoning ability, but it is not easy to understand the deep semantic feature vector extracted by transformer as deep learning is a “black-box.”

Summary

Introduction

Alzheimer’s disease (AD) is a nervous degenerative disease with an insidious and irreversible onset, which is difficult to be detected in every stage. AD can influence patients’ daily living ability and social communicate ability and may even lead to disability [1, 2]. Researchers have found that AD has a profound impact on patients’ language function [3] in addition to mood, attention, memory, movement, and so on. Language is the representation of mental activities, which can clearly reflect the relationship among language, cognition, and communication [4]. Language interference is a common manifestation of patients [5] with AD which may even earlier than orientation and memory difficulties [6, 7]. Taken from Boston Aphasia Diagnostic Test [8], has already been verified sensitive to subtle cognitive deficits [9]; valuable clinical information can be obtained from spontaneous speech to recognize AD. The transcripts of speech can be used to detect AD effectively

Methods

Results

Discussion

Conclusion