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Review
. 2022 Jan 11;9(1):27.
doi: 10.3390/bioengineering9010027.

Speech- and Language-Based Classification of Alzheimer's Disease: A Systematic Review

Inês Vigo  1 , Luis Coelho  1   2 , Sara Reis  1   2
Affiliations
Review

Speech- and Language-Based Classification of Alzheimer's Disease: A Systematic Review

Inês Vigo et al. Bioengineering (Basel). .

Abstract

Background: Alzheimer's disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure.

Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer's Disease with the purpose of identifying the most effective algorithms and best practices.

Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori.

Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported.

Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

Keywords: Alzheimer’s disease (AD); classification; features; machine learning (ML); mild cognitive impairment (MCI); speech.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Number of publications (within the review’s scope) by year, in absolute value.
Figure 2
Figure 2
Flow chart of the different phases of the review.
Figure 3
Figure 3
Flowchart of a general machine learning pipeline to process acoustic/prosodic correlates of disease. Adapted from Braga et al. [31].
Figure 4
Figure 4
Prevalence of classification models.
Figure 5
Figure 5
Mean accuracy by classification model.
See this image and copyright information in PMC

References

    1. Brookmeyer R., Johnson E., Ziegler-Graham K., Arrighi H.M. Forecasting the Global Burden of Alzheimer’s Disease. Alzheimer’s Dement. 2007;3:186–191. doi: 10.1016/j.jalz.2007.04.381. - DOI - PubMed
    1. Prince M., Bryce R., Albanese E., Wimo A., Ribeiro W., Ferri C.P. The Global Prevalence of Dementia: A Systematic Review and Metaanalysis. Alzheimer’s Dement. 2013;9:63–75.e2. doi: 10.1016/j.jalz.2012.11.007. - DOI - PubMed
    1. Khachaturian Z.S. Diagnosis of Alzheimer’s Disease. Arch. Neurol. 1985;42:1097–1105. doi: 10.1001/archneur.1985.04060100083029. - DOI - PubMed
    1. Weller J., Budson A. Current Understanding of Alzheimer’s Disease Diagnosis and Treatment. F1000Res. 2018;7:F1000 Faculty Rev-1161. doi: 10.12688/f1000research.14506.1. - DOI - PMC - PubMed
    1. Pereira T., Ferreira F.L., Cardoso S., Silva D., de Mendonça A., Guerreiro M., Madeira S.C., for the Alzheimer’s Disease Neuroimaging Initiative Neuropsychological Predictors of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Feature Selection Ensemble Combining Stability and Predictability. BMC Med. Inform. Decis. Mak. 2018;18:137. doi: 10.1186/s12911-018-0710-y. - DOI - PMC - PubMed

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