An assessment system for Alzheimer’s disease based on speech using a novel feature sequence design and recurrent neural network
Abstract
Alzheimer disease and other dementias have become the 7th cause of death worldwide. Still lacking a cure, an early detection of the disease in order to provide the best intervention is crucial. To develop an assessment system for the general public, speech analysis is the optimal solution since it reflects the speaker's cognitive skills abundantly and data collection is relatively inexpensive. While most of the related studies extracted statistics-based features and relied on a feature selection process, we have proposed a novel Feature Sequence representation and utilized a recurrent neural network to perform classification in this paper. To validate our work, an experiment has been conducted with 150 speech samples, and the score in terms of the area under the receiver operating characteristic curve is as high as 0.954, potentially outperforming the current state-of-the-art method.
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Papers lacking health assessment information do not specify their criteria for group assignment.
“Therefore, papers lacking that information do not specify their criteria for group assignment [60, 61, 75, 79, 80, 82–85].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [82]
Very few reviewed papers report use of artificial neural networks, recurrent neural networks, multi-layer perceptron, or convolutional neural networks.
“As regards choice of machine learning methods, very few papers report the use of artificial neural networks [91, 95], recurrent neural networks [82], multi-layer perceptron [44, 67, 79, 80, 86], or convolutional neural networks [60, 85].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [82]
Cross-validation is reported in all reviewed papers but five.
“Cross-validation (CV) is probably the most established practice for classifier evaluation. It is reported in all papers but five, of which two are not very recent [66, 93], another two do not report CV but report using a hold-out set [82, 91], and only one reports using neither CV nor a hold-out set procedure [95].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [82]
Apart from English, only Portuguese and Chinese have been researched exclusively with text features.
“Apart from English, only Portuguese and Chinese have been researched exclusively with text features [57, 82, 90].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [82]
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de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200888 · Cited by 5 · see citing works
- Authors
- Chien, Y.-W., Hong, S.-Y., Cheah, W.-T., Fu, L.-C., & Chang, Y.-L.
- Affiliations
- National Taiwan University (TW)
- Venue
- 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 3289-3294
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W2909308058
- Cited by
- 15
- References
- 21
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 1 · 2022: 5 · 2021: 2 · 2020: 3
- FWCI
- 3.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease, dementia, speech analysis, early detection, recurrent neural networks, speech classification, cognitive impairment assessment
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