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Published in: Cognitive Neurodynamics 6/2018

27-08-2018 | Research Article

Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease

Authors: Mahda Nasrolahzadeh, Zeynab Mohammadpoory, Javad Haddadnia

Published in: Cognitive Neurodynamics | Issue 6/2018

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Abstract

An early and accurate diagnosis of Alzheimer’s disease (AD) has been progressively attracting more attention in recent years. One of the main problems of AD is the loss of language skills. This paper presents a computational framework for classifying AD patients compared to healthy control subjects using information from spontaneous speech signals. Spontaneous speech data are obtained from 30 AD patients and 30 healthy controls. Because of the nonlinear and dynamic nature of speech signals, higher order spectral features (specifically bispectrum) were used for analysis. Four classifiers (k-Nearest Neighbor, Support Vector Machine, Naïve Bayes and Decision tree) were used to classify subjects into three different levels of AD and healthy group based on their performance in terms of the HOS-based features. Ten-fold cross-validation method was used to test the reliability of the classifier results. The results showed that the proposed method had a good potential in AD diagnosis. The proposed method was also able to diagnose the earliest stage of AD with high accuracy. The method has the great advantage of being non-invasive, cost-effective, and associated with no side effects. Therefore, the proposed method can be a spontaneous speech directed test for pre-clinical evaluation of AD diagnosis.

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Metadata
Title
Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease
Authors
Mahda Nasrolahzadeh
Zeynab Mohammadpoory
Javad Haddadnia
Publication date
27-08-2018
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 6/2018
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-018-9499-8

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