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Advisor(s)
Abstract(s)
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.
Description
Keywords
Alzheimer’s disease (AD) Speech Classification Features Machine learning (ML) Mild cognitive impairment (MCI)
Citation
Publisher
MDPI