An Automatic System for Dementia Detection using Acoustic and Linguistic Features

Gonzalez-Atienza, Miriam; Peinado, Antonio M.; Gonzalez-Lopez, José A.

doi:10.21437/iberspeech.2021-56

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…In recent years, some research has been performed in which the use of the acoustic features extracted from the neuropsychological test’s audio records, in addition to the linguistic features, has been proposed [ 30 , 31 , 32 , 33 ]. These works use the ADReSS Challenge database [ 34 ], which was formed to perform two main tasks: the first is an MMSE score regression task that is used to create a model to infer the subject’s MMSE score, on the basis of the speech production during a neuropsychological assessment; and the second is an AD classification task, where the production of a model to predict the label of “AD” or “non-AD” for a speech session is required.…”

Section: Related Workmentioning

confidence: 99%

Semantic Feature Extraction Using SBERT for Dementia Detection

et al. 2022

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Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer’s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used.

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Section: Related Workmentioning

confidence: 99%

Semantic Feature Extraction Using SBERT for Dementia Detection

et al. 2022

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“…In the early-stage subjects with dementia often experience difficulties in finding the right word, forming coherent sentences, loss of verbal fluency, slow and hesitant speech [18,19]. Moreover, linguistic features (i.e., part of speech, word count, and word embeddings), and acoustic features (i.e., spectral features, pauses, and speech rhythm) in spontaneous speech can serve as features for identifying cognitive decline [20,21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Al-Hammadi,

Fleyeh,

Åberg

et al. 2024

JAD

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Background: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer’s disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

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Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction

Sprint

Cook

Schmitter‐Edgecombe

et al. 2022

ACM Trans. Comput. Healthcare

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New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this paper, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to ten clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from r = 0.601 to r = 0.871 for each clinical assessment.

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An Automatic System for Dementia Detection using Acoustic and Linguistic Features

Cited by 3 publications

References 24 publications

Semantic Feature Extraction Using SBERT for Dementia Detection

Semantic Feature Extraction Using SBERT for Dementia Detection

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction

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