A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints
Open accessAbstract
Neurodegenerative diseases causing dementia are known to affect a person's speech and language. Part of the expert assessment in memory clinics therefore routinely focuses on detecting such features. The current outpatient procedures examining patients' verbal and interactional abilities mainly focus on verbal recall, word fluency, and comprehension. By capturing neurodegeneration-associated characteristics in a person's voice, the incorporation of novel methods based on the automatic analysis of speech signals may give us more information about a person's ability to interact which could contribute to the diagnostic process. In this proof-of-principle study, we demonstrate that purely acoustic features, extracted from recordings of patients' answers to a neurologist's questions in a specialist memory clinic can support the initial distinction between patients presenting with cognitive concerns attributable to progressive neurodegenerative disorders (ND) or Functional Memory Disorder (FMD, i.e., subjective memory concerns unassociated with objective cognitive deficits or a risk of progression). The study involved 15 FMD and 15 ND patients where a total of 51 acoustic features were extracted from the recordings. Feature selection was used to identify the most discriminating features which were then used to train five different machine learning classifiers to differentiate between the FMD/ND classes, achieving a mean classification accuracy of 96.2%. The discriminative power of purely acoustic approaches could be integrated into diagnostic pathways for patients presenting with memory concerns and are computationally less demanding than methods focusing on linguistic elements of speech and language that require automatic speech recognition and understanding.
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In Alzheimer's disease, ASLA markers differentiate patients from healthy persons and other patient groups, predict overall cognitive status, outperform certain cognitive tests in predicting dementia onset, and correlate with volume of the hippocampus and other core atrophy regions.
“In Alzheimer's disease, they differentiate patients from healthy persons 13,58-62 and other patient groups, 63 predict overall cognitive status, 64 outperform certain cognitive tests in predicting dementia onset, 65 and correlate with volume of the hippocampus and other core atrophy regions. 66,67”
From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [64]
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910 · see citing works
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
Studies of interference in serial verbal reactions
Stroop, J. R. (1935) · Journal of Experimental Psychology · doi:10.1037/h0054651 · Cited by 18,622 · see citing works
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · see citing works
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980 · see citing works
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,843 · see citing works
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465 · see citing works
Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia
Rascovsky, K., Hodges, J. R., Knopman, D., Mendez, M. F., Kramer, J. H., Neuhaus, J., van Swieten, J. C., Seelaar, H., Dopper, E. G. P., Onyike, C. U., et al. (2011) · Brain · doi:10.1093/brain/awr179 · Cited by 5,221 · see citing works
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417 · see citing works
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321 · see citing works
Appelhof, B., Bakker, C., den IJssel, J. C. L. V. D., Zwijsen, S. A., Smalbrugge, M., Verhey, F. R. J., de Vugt, M. E., Zuidema, S. U., & Koopmans, R. T. C. M. (2017) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000477087 · Cited by 2,773 · see citing works
Albert, M., DeKosky, S. T., Dickson, D. W., Dubois, B., Feldman, H., Fox, N. C., Gamst, A. C., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2013) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.11.1.96 · Cited by 2,630 · see citing works
Feature selection for high-dimensional data: a fast correlation-based filter solution
Yu, L., & Liu, H. (2003) · Cited by 2,208 · see citing works
Mioshi, E., Dawson, K., Mitchell, J., Arnold, R., & Hodges, J. R. (2006) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.1610 · Cited by 2,016 · see citing works
YIN, a fundamental frequency estimator for speech and music
de Cheveigné, A., & Kawahara, H. (2002) · The Journal of the Acoustical Society of America · doi:10.1121/1.1458024 · Cited by 1,927 · see citing works
(2010) · doi:10.5555/1756006.1859921 · Cited by 1,924 · see citing works
Mild cognitive impairment: a concept in evolution
Petersen, R. C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014) · Journal of Internal Medicine · doi:10.1111/joim.12190 · Cited by 1,694 · see citing works
Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV)
Lani, J. A., & Wechsler, D. (2010) · Cited by 1,686 · see citing works
A review of feature selection methods based on mutual information
Vergara, J. R., & Estévez, P. A. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1368-0 · Cited by 1,296 · see citing works
Normative Data and Screening Power of a Shortened Version of the Token Test
Renzi, E. D., & FAGLIONI, P. (1978) · Cortex · doi:10.1016/s0010-9452(78)80006-9 · Cited by 1,131 · see citing works
Anthony, J. C., LeResche, L., Niaz, U., Korff, M. R. V., & Folstein, M. F. (1982) · Psychological Medicine · doi:10.1017/s0033291700046730 · Cited by 1,105 · see citing works
Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech TestsPreprint
Tsanas, A., Little, M. A., McSharry, P., & Ramig, L. O. (2009) · Nature Precedings · doi:10.1109/tbme.2009.2036000 · Cited by 833 · see citing works
Linguistic Features Identify Alzheimer’s Disease in Narrative Speech
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150520 · Cited by 797 · see citing works
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson’s Disease
Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2183367 · Cited by 732 · see citing works
Guide to using the Coloured Progressive Matrices.
Raven, J. (1958) · Cited by 667 · see citing works
Li, T., Zhang, C., & Ogihara, M. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth267 · Cited by 648 · see citing works
Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease
König, A., Satt, A., Sorin, A., Hoory, R., Toledo‐Ronen, O., Derreumaux, A., Manera, V., Verhey, F., Aalten, P., Robert, P. H., et al. (2015) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2014.11.012 · Cited by 380 · see citing works
Spoken Language Derived Measures for Detecting Mild Cognitive Impairment
Roark, B., Mitchell, M., Hosom, J.-P., Hollingshead, K., & Kaye, J. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2112351 · Cited by 355 · see citing works
Davis, S. B., & Mermelstein, P. G. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50010-3 · Cited by 341 · see citing works
Tsanas, A., Little, M. A., McSharry, P., & Ramig, L. O. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0456 · Cited by 323 · see citing works
Innovative diagnostic tools for early detection of Alzheimer's disease
Laske, C., Sohrabi, H. R., Frost, S. M., López-de-Ipiña, K., Garrard, P., Buscema, M., Dauwels, J., Soekadar, S. R., Mueller, S., Linnemann, C., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.06.004 · Cited by 316 · see citing works
Benson, N., Hulac, D. M., & Kranzler, J. H. (2010) · Psychological Assessment · doi:10.1037/a0017767 · Cited by 309 · see citing works
Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., & Kalman, J. (2018) · Current Alzheimer Research · doi:10.2174/1567205014666171121114930 · Cited by 290 · see citing works
Brain PET in Suspected Dementia: Patterns of Altered FDG Metabolism
Brown, R. K. J., Bohnen, N. I., Wong, K. K., Minoshima, S., & Frey, K. A. (2014) · RadioGraphics · doi:10.1148/rg.343135065 · Cited by 256 · see citing works
Szatloczki, G., Hoffmann, I., Vincze, V., Kalman, J., & Pakaski, M. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00195 · Cited by 249 · see citing works
Gosztolya, G., Vincze, V., Tóth, L., Pákáski, M., Kálmán, J., & Hoffmann, I. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2018.07.007 · Cited by 196 · see citing works
López-de-Ipiña, K., Alonso, J.-B., Travieso, C., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M., Ezeiza, A., Barroso, N., Ecay-Torres, M., Martinez-Lage, P., et al. (2013) · Sensors · doi:10.3390/s130506730 · Cited by 190 · see citing works
MacWhinney, B. (2007) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230223936_7 · Cited by 187 · see citing works
Aided diagnosis of dementia type through computer-based analysis of spontaneous speech
Jarrold, W., Peintner, B., Wilkins, D., Vergryi, D., Richey, C., Gorno-Tempini, M. L., & Ogar, J. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3204 · Cited by 178 · see citing works
Diagnostic value of quantitative EEG in Alzheimer’s disease
Bennys, K., Rondouin, G., Vergnes, C., & Touchon, J. (2001) · Neurophysiologie Clinique · doi:10.1016/s0987-7053(01)00254-4 · Cited by 176 · see citing works
Brain SPECT in neurology and psychiatry.
Camargo, E. E. (2001) · PubMed · Cited by 164 · see citing works
Speech in Alzheimer's Disease: Can Temporal and Acoustic Parameters Discriminate Dementia?
Meilán, J. J. G., Martínez‐Sánchez, F., Carro, J., López, D. E., Millian-Morell, L., & Martínez, J. M. A. (2014) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000356726 · Cited by 149 · see citing works
Spectral subband centroid features for speech recognition
Paliwal, K. K. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.675340 · Cited by 148 · see citing works
Jones, D., Drew, P., Elsey, C., Blackburn, D. J., Wakefield, S., Harkness, K., & Reuber, M. (2015) · Aging & Mental Health · doi:10.1080/13607863.2015.1021753 · Cited by 146 · see citing works
Self-citationLópez-de-Ipiña, K., Alonso, J. B., Solé-Casals, J., Barroso, N., Henriquez, P., Faundez-Zanuy, M., Travieso, C. M., Ecay-Torres, M., Martínez-Lage, P., & Eguiraun, H. (2015) · Cognitive Computation · doi:10.1007/s12559-013-9229-9 · Cited by 132 · see citing works
Elsey, C., Drew, P., Jones, D., Blackburn, D. J., Wakefield, S., Harkness, K., Venneri, A., & Reuber, M. (2015) · Patient Education and Counseling · doi:10.1016/j.pec.2015.05.021 · Cited by 114 · see citing works
Self-citationCommissaris, C. J. A. M., Ponds, R., & Jolles, J. (1998) · Patient Education and Counseling · doi:10.1016/s0738-3991(98)00040-8 · Cited by 111 · see citing works
Gayraud, F., Lee, H., & Barkat-Defradas, M. (2010) · Clinical Linguistics & Phonetics · doi:10.3109/02699206.2010.521612 · Cited by 96 · see citing works
The Syndrome of Functional Memory Disorder: Definition, Etiology, and Natural Course
Schmidtke, K., Pohlmann, S., & Metternich, B. (2008) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e318187ddf9 · Cited by 91 · see citing works
Metternich, B., Schmidtke, K., & Hüll, M. (2009) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2008.07.005 · Cited by 87 · cited 1× · see citing works
“amyloid and tau testing in the cerebrospinal fluid) and not suitable for for screening at the interface between primary and specialist care patients [6].”
BackgroundClassification of speech intelligibility in Parkinson's disease
Khan, T., Westin, J., & Dougherty, M. S. (2013) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2013.10.003 · Cited by 84 · see citing works
Evaluation of speech-based protocol for detection of early-stage dementia
Satt, A., Sorin, A. S., Ronen, O., Barkan, O., Kompatsiaris, I. Y., Kokonozi, A., & Tsolaki, M. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-32 · Cited by 78 · see citing works
Speech-Based Detection of Alzheimer’s Disease in Conversational German
Weiner, J., Herff, C., & Schultz, T. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-100 · Cited by 77 · see citing works
Investigation of spectral centroid features for cognitive load classification
Le, P. N., Eliathamby Ambikairajah, A., Epps, J., Sethu, V., & Choi, E. H. C. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.005 · Cited by 74 · see citing works
Addenbrooke's Cognitive Examination-Revised (ACE-R) in day-to-day clinical practice
Larner, A. J. (2007) · Age and Ageing · doi:10.1093/ageing/afm112 · Cited by 70 · see citing works
Evaluation of an objective technique for analysing temporal variables in DAT spontaneous speech
Singh, S., Bucks, R. S., & Cuerden, J. M. (2001) · Aphasiology · doi:10.1080/02687040143000041 · Cited by 67 · see citing works
Blackburn, D. J., Wakefield, S., Shanks, M. F., Harkness, K. A., Reuber, M., & Venneri, A. (2014) · British Medical Bulletin · doi:10.1093/bmb/ldu029 · Cited by 67 · see citing works
Self-citationUtilidad de los test breves en el cribado de demencia
Villarejo‐Galende, A., & Puertas‐Martín, V. (2011) · Neurología · doi:10.1016/j.nrl.2010.12.002 · Cited by 63 · see citing works
de Ipiña, K. L., Solé‐Casals, J., Eguiraun, H., Alonso, J. B., Travieso, C. M., Ezeiza, A., Barroso, N., Ecay‐Torres, M., Martínez‐Lage, P., & Beitia, B. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.08.002 · Cited by 58 · see citing works
Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints
Mirheidari, B., Blackburn, D., Harkness, K., Walker, T., Venneri, A., Reuber, M., & Christensen, H. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160507 · Cited by 54 · see citing works
Self-citationCreating and Digitizing Language Corpora
Barbiers, S., Corrigan, K. P., & Mearns, A. (2016) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-137-38645-8 · Cited by 47 · see citing works
Simple and robust audio-based detection of biomarkers for Alzheimer's disease
Al-Hameed, S., Benaissa, M., & Christensen, H. (2016) · doi:10.21437/slpat.2016-6 · Cited by 42 · see citing works
Self-citationAn Avatar-Based System for Identifying Individuals Likely to Develop Dementia
Mirheidari, B., Blackburn, D. J., Harkness, K., Walker, T., Venneri, A., Reuber, M., & Christensen, H. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-690 · Cited by 29 · see citing works
Self-citationDetecting and Predicting Alzheimer's Disease Severity in Longitudinal Acoustic Data
Al-Hameed, S., Benaissa, M., & Christensen, H. (2017) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3175587.3175589 · Cited by 28 · see citing works
Self-citationDiagnosing People with Dementia Using Automatic Conversation Analysis
Mirheidari, B., Blackburn, D., Reuber, M., Walker, T., & Christensen, H. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-857 · Cited by 25 · see citing works
Self-citationCreating and Digitizing Language Corpora
Beal, J. C., Corrigan, K. P., & Moisl, H. L. (2007) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230223936 · Cited by 22 · see citing works
Usefulness of short tests in dementia screening
Villarejo‐Galende, A., & Puertas‐Martín, V. (2011) · Neurología (English Edition) · doi:10.1016/j.nrleng.2010.12.001 · Cited by 20 · see citing works
Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis
de Ipiña, K. L., Alonso, J. B., Solé-Casals, J., et al. (2012) · International Joint Conference on Computational Intelligence · doi:10.5220/0004188606980705 · Cited by 18 · see citing works
de Ipiña, K. L., Alonso, J. B., Travieso, C. M., Eguiraun, H., Ecay, M., Ezeiza, A., Barroso, N., & Martínez‐Lage, P. (2013) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines.2013.6632783 · Cited by 16 · see citing works
Faure‐Bardon, V., Mandelbrot, L., Duro, D., Dussaux, C., Lê, M., & Peytavin, G. (2017) · AIDS · doi:10.1097/qad.0000000000001681 · Cited by 10 · see citing works
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Speech and language markers of neurodegeneration: a call for global equity
García, A. M., de Leon, J., Tee, B. L., Blasi, D. E., & Gorno-Tempini, M. L. (2023) · Brain · doi:10.1093/brain/awad253 · Cited by 102 · see citing works
Introduction to large language models (LLMs) for dementia care and research
Treder, M. S., Lee, S., & Tsvetanov, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1385303 · Cited by 63 · see citing works
Clinical signs in functional cognitive disorders: A systematic review and diagnostic meta-analysis
Cabreira, V., Frostholm, L., McWhirter, L., Stone, J., & Carson, A. (2023) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2023.111447 · Cited by 43 · see citing works
Cabreira, V., Alty, J., Antic, S., Araújo, R., Aybek, S., Ball, H. A., Baslet, G., Bhome, R., Coebergh, J., Dubois, B., et al. (2025) · European Journal of Neurology · doi:10.1111/ene.16318 · Cited by 24 · see citing works
- Authors
- Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., & Reuber, M.
- Affiliations
- University of Sheffield (GB)
- Royal Hallamshire Hospital (GB)
- Corresponding author
- Al-Hameed, Sabah
- Benaissa, Mohammed
- Christensen, Heidi
- Mirheidari, Bahman
- Blackburn, Daniel
- Reuber, Markus
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-05-24
- Volume
- 14
- Issue
- 5
- Pages
- e0217388
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6534304
- OpenAlex
- W2945932771
- Cited by
- 37
- References
- 78
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 10 · 2022: 4 · 2021: 6 · 2020: 3
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research (PB-PG-0211-24079)
- Medical Research Council (MC_PC_14115)
- Research for Patient Benefit Programme
- NIHR Sheffield Biomedical Research Centre
- 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
- neurodegenerative diseases, dementia, subjective memory complaints, acoustic features, speech analysis, voice analysis, feature selection, machine learning classification, early detection of neurodegeneration
- MeSH terms
- Machine Learning, Proof of Concept Study, Aged, Dementia, Diagnosis, Computer-Assisted, Diagnosis, Differential, Female, Humans, Male, Memory Disorders, Middle Aged, Sound Spectrography, Speech Acoustics, Video Recording, Neurodegenerative Diseases, Cognitive Dysfunction
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