基于离散变分自编码器BERT模型的经颅聚焦超声检测阿尔茨海默病

IF 2.7 4区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Kaushika Reddy Thipparthy , Archana Kollu , Chaitanya Kulkarni , Ashit Kumar Dutta , Hardik Doshi , Aditya Kashyap , Kumari Priyanka Sinha , Suresh Babu Kondaveeti , Rupesh Gupta
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引用次数: 0

摘要

研究背景:阿尔茨海默病(AD)是一种神经退行性疾病,其症状包括失语和语言流畅性下降。研究人员利用语音属性、流利性、停顿和各种副语言特征,或从转录文本衍生的方面来识别阿尔茨海默病。然而,传统的基于声学特征的检测技术在捕获语义信息的能力方面受到限制,并且将语音转录为文本的过程既耗时又费力。非侵入性脑刺激(NBS),包括经颅磁刺激(TMS)和经颅聚焦超声(tFUS)等方法,已被研究作为一种潜在的干预措施,以增强阿尔茨海默病患者的认知功能和沟通,显示出调节大脑活动和促进神经可塑性的疗效。本研究利用离散变分自编码器将语音转换为伪音素序列,随后应用BERT(双向编码器表示从变压器)模型来分析这些伪音素序列之间的关系。本研究提出一个tFUS-BERT模型来封装音频的语言表征。结果分析所提出的tFUS-BERT模型在与Wav2vec 2.0和Hu-BERT结合时的准确率分别为76.06 %和71.83 %,在ADReSSo数据集上优于基线5.63 %。此外,与传统的声学方法相比,该模型在捕捉语言表征方面表现出优越的性能,展示了其在准确和可扩展的阿尔茨海默病检测方面的潜力。与之前的研究相比,该模型在addresso(仅通过自发语音识别阿尔茨海默氏痴呆症)数据集上的准确率为70.42 %,与基线系统相比提高了5.63 %。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discrete variational autoencoders BERT model-based transcranial focused ultrasound for Alzheimer's disease detection

Research background

Alzheimer's Disease (AD) is a neurodegenerative condition marked by symptoms including aphasia and diminished verbal fluency. Researchers have employed phonetic attributes, fluency, pauses, and various paralinguistic traits, or derived aspects from transcribed text, to identify Alzheimer's disease.

Methods and methodology

Nevertheless, conventional acoustic feature-based detection techniques are constrained in their ability to capture semantic information, and the process of transcribing speech into text is both time-consuming and labour-intensive. Non-invasive brain stimulation (NBS), encompassing methods such as transcranial magnetic stimulation (TMS) and Transcranial focused ultrasound (tFUS), has been investigated as a potential intervention to enhance cognitive functions and communication in Alzheimer's patients, demonstrating efficacy in modulating brain activity and promoting neuroplasticity. This research utilises Discrete Variational Autoencoders to transform speech into pseudo-phoneme sequences, subsequently applying the BERT (Bidirectional Encoder Representations from Transformers) model to analyse the relationships among these pseudo-phoneme sequences. This research proposes a tFUS-BERT model to encapsulate the linguistic representations of audio.

Result analysis

The proposed tFUS-BERT model demonstrated its effectiveness with an accuracy of 76.06 % when combined with Wav2vec 2.0 and 71.83 % with Hu-BERT, outperforming the baseline by 5.63 % on the ADReSSo dataset. Additionally, the model exhibited superior performance in capturing linguistic representations compared to traditional acoustic methods, showcasing its potential for accurate and scalable Alzheimer's detection.

Comparison with previous studies

The model attains an accuracy of 70.42 % on the ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech Only) dataset, reflecting a 5.63 % enhancement compared to the baseline system.
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来源期刊
Journal of Neuroscience Methods
Journal of Neuroscience Methods 医学-神经科学
CiteScore
7.10
自引率
3.30%
发文量
226
审稿时长
52 days
期刊介绍: The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.
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