Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease
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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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Out of 51 reviewed papers, only seven did three-way or four-way classification.
“Out of 51 reviewed papers, only seven did attempt three-way [50, 62–64] or four-way [12, 65, 66] classification.”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [65]
Seven reviewed papers are concerned with three or four disease stages.
“Outside of those, seven papers are concerned with three or four disease stages [12, 50, 62–66], two explore longitudinal cognitive changes (although still through binary classification) [67, 68], and one describes discourse patterns [69].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [65]
Nasrolahzadeh et al. reported 97.71% accuracy with a 4-way classifier in a highly imbalanced dataset.
“Nasrolahzadeh et al. [65] report really high accuracy with a 4-way classifier, 97.71%, but in a highly imbalanced dataset.”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [65]
Only two reviewed papers rely on truly spontaneous and natural monologues, prompted with an open question instead of a picture description.
“Only two papers rely on truly spontaneous and natural monologues, prompted with an open question instead of a picture description [60, 65].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [65]
Research using non-English speech data almost invariably includes acoustic features, either as part of a larger feature set or exclusively.
“research undertaken using non-English speech data almost invariably includes acoustic features, either as part of a larger feature set”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [65]
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Self-citation
- Authors
- Nasrolahzadeh, M., Mohammadpoory, Z., & Haddadnia, J.
- Affiliations
- Hakim Sabzevari University (IR)
- Corresponding author
- Nasrolahzadeh, Mahda
- Venue
- Cognitive Neurodynamics
- Published
- 2018-08-27
- Volume
- 12
- Issue
- 6
- Pages
- 583-596
- ISSN
- 1871-4080 (ISSN-L)
- 1871-4099
- PMC
- PMC6233329
- OpenAlex
- W2888982149
- Cited by
- 51
- References
- 66
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 2 · 2023: 11 · 2022: 4 · 2021: 7 · 2020: 8 · 2019: 7
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Cognitive Neurodynamics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, spontaneous speech, higher-order spectral analysis, bispectrum, speech-based diagnosis, early diagnosis, language impairment, non-invasive biomarkers, preclinical evaluation
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