Paralinguistic and linguistic fluency features for Alzheimer's disease detection

Campbell, Edward L.; Mesía, Raúl Yañez; Docío-Fernández, Laura; Garcı́a-Mateo, Carmen

doi:10.1016/j.csl.2021.101198

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Computational techniques based on the automatic processing of voice signals have found an important place in research applied to the detection of neurodegenerative disorders. With the aim of developing a method to generalize the use of computational tools for the diagnosis of Alzheimer's disease, Campbell et al (10) propose, in their study, the use of multilingual systems with characteristics extracted from voice signals. One of the main objectives of the research was to explore the advantages of using paralinguistic parameters to develop a multilingual method of fully automatic disease detection.…”

Section: Related Workmentioning

confidence: 99%

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Identificação da Doença de Alzheimer Através da Fala Utilizando Reconhecimento de Emoções

Bernieri

Duarte

2023

J Health Inform

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Alzheimer's disease is the most common neurodegenerative dementia in elderly people in the world and its diagnosis requires a wide medical evaluation, supported by cognitive tests, clinical and imaging exams. Identifying the disease through speech can reduce the cost and time of medical diagnosis. Emotional states are important performance indicators of cognitive processes. Intelligent and non-invasive computational techniques can become relevant support tools for an early medical diagnosis. Therefore, this article addresses the use of emotion recognition through voice as a biomarker to identify the presence of Alzheimer's disease. The proposed method is based on the extraction of emotional features from speech and pattern recognition using neural networks. The results of the experiments reached an accuracy of 72.61%, a precision of 72.90% and a recall of 72.50% through cross-validation of the data.

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Section: Related Workmentioning

confidence: 99%

“…This study uses two datasets widely used in research of this nature, as can be seen in the related works of Campbell et al (10) and Haider et al (8) . The Berlin Database of Emotional Speech (Emo-DB) (11) was used to train the automatic emotion classifier and the Pitt Corpus (12) was used for the classification of Alzheimer's disease.…”

Section: Datasetsmentioning

confidence: 99%

Identificação da Doença de Alzheimer Através da Fala Utilizando Reconhecimento de Emoções

Bernieri

Duarte

2023

J Health Inform

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“…Using silence-based features (e.g., silence segment, filled pauses, and silence duration) with a machine learning technique has yielded an F1-score of 78.8% for detecting MCI [ 15 ]. Recently, Campbell et al [ 32 ] proposed an algorithm based on analyzing the temporal patterns of silence in VF tasks using the “AcceXible” and “ADReSS” databases. Their results showed that the silence-based feature had the best accuracy in the VF tasks.…”

Section: Related Workmentioning

confidence: 99%

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments

Metarugcheep

Punyabukkana

Wanvarie

et al. 2022

Sensors

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Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as “F” in English and “ก” /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data.

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“…The distribution of speech pauses has been described as a marker for AD diagnosis [ 48 ]. Studies have shown that identification of language and diarization of speakers provide promising results for the diagnosis of speech loss in the case of AD [ 49 ].…”

Section: Shared Symptoms Of Asd and Admentioning

confidence: 99%

Symptomatic, Genetic, and Mechanistic Overlaps between Autism and Alzheimer’s Disease

et al. 2021

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Autism spectrum disorder (ASD) and Alzheimer’s disease (AD) are neurodevelopmental and neurodegenerative disorders affecting two opposite ends of life span, i.e., childhood and old age. Both disorders pose a cumulative threat to human health, with the rate of incidences increasing considerably worldwide. In the context of recent developments, we aimed to review correlated symptoms and genetics, and overlapping aspects in the mechanisms of the pathogenesis of ASD and AD. Dementia, insomnia, and weak neuromuscular interaction, as well as communicative and cognitive impairments, are shared symptoms. A number of genes and proteins linked with both disorders have been tabulated, including MECP2, ADNP, SCN2A, NLGN, SHANK, PTEN, RELN, and FMR1. Theories about the role of neuron development, processing, connectivity, and levels of neurotransmitters in both disorders have been discussed. Based on the recent literature, the roles of FMRP (Fragile X mental retardation protein), hnRNPC (heterogeneous ribonucleoprotein-C), IRP (Iron regulatory proteins), miRNAs (MicroRNAs), and α-, β0, and γ-secretases in the posttranscriptional regulation of cellular synthesis and processing of APP (amyloid-β precursor protein) have been elaborated to describe the parallel and overlapping routes and mechanisms of ASD and AD pathogenesis. However, the interactive role of genetic and environmental factors, oxidative and metal ion stress, mutations in the associated genes, and alterations in the related cellular pathways in the development of ASD and AD needs further investigation.

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Paralinguistic and linguistic fluency features for Alzheimer's disease detection

Cited by 12 publications

References 25 publications

Identificação da Doença de Alzheimer Através da Fala Utilizando Reconhecimento de Emoções

Identificação da Doença de Alzheimer Através da Fala Utilizando Reconhecimento de Emoções

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments

Symptomatic, Genetic, and Mechanistic Overlaps between Autism and Alzheimer’s Disease

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