Publication detail

Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach

LOPEZ-DE-IPINA, K. MARTINEZ-DE-LIZARDUY, U. CALVO, P.M. MEKYSKA, J. BEITIA, B. BARROSO, N. ESTANGA, A. TAINTA, M. ECAY-TORRES, M.

Original Title

Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach

Type

journal article in Web of Science

Language

English

Original Abstract

Objective: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Methods: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. Results: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Conclusion: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.

Keywords

Alzheimer's disease; deep learning; emotion analysis; innovative tools; multi-tasks; speech processing; spontaneous speech

Authors

LOPEZ-DE-IPINA, K.; MARTINEZ-DE-LIZARDUY, U.; CALVO, P.M.; MEKYSKA, J.; BEITIA, B.; BARROSO, N.; ESTANGA, A.; TAINTA, M.; ECAY-TORRES, M.

Released

1. 2. 2018

ISBN

1567-2050

Periodical

Current Alzheimer Research

Year of study

15

Number

2

State

United Arab Emirates

Pages from

139

Pages to

148

Pages count

10

URL

http://www.eurekaselect.com/157381/article

BibTex

@article{BUT147731,
  author="LOPEZ-DE-IPINA, K. and MARTINEZ-DE-LIZARDUY, U. and CALVO, P.M. and MEKYSKA, J. and BEITIA, B. and BARROSO, N. and ESTANGA, A. and TAINTA, M. and ECAY-TORRES, M.",
  title="Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach",
  journal="Current Alzheimer Research",
  year="2018",
  volume="15",
  number="2",
  pages="139--148",
  doi="10.2174/1567205014666171120143800",
  issn="1567-2050",
  url="http://www.eurekaselect.com/157381/article"
}