Two-stage feature selection of voice parameters for early Alzheimer’s disease prediction
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [50]
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 [50]
Mirzaei et al. reported 62% for a 3-way classification discriminating HC, MCI, and AD.
“Mirzaei et al. [50] reports 62% for a 3-way classification, discriminating HC, MCI, and AD.”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [50]
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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · 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
Toward optimal feature selection
Koller, D., & Sahami, M. (1996) · Cited by 1,436 · see citing works
Boersma, P. (1993) · Cited by 1,042 · 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
THE DECLINE OF WORKING MEMORY IN ALZHEIMER'S DISEASE
Baddeley, A., Bressi, S., Sala, S. D., Logie, R. H., & Spinnler, H. R. (1991) · Brain · doi:10.1093/brain/114.6.2521 · Cited by 699 · see citing works
Harmonics-to-noise ratio as an index of the degree of hoarseness
Yumoto, E., Gould, W. J., & Baer, T. (1982) · The Journal of the Acoustical Society of America · doi:10.1121/1.387808 · Cited by 562 · 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
Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease
Ahmed, S., Haigh, A.-M. F., de Jager, C. A., & Garrard, P. (2013) · Brain · doi:10.1093/brain/awt269 · Cited by 362 · see citing works
Toward optimal feature selection using ranking methods and classification algorithms
Novakovic, J., Štrbac, P., & Bulatović, D. (2011) · Yugoslav journal of operations research · doi:10.2298/yjor1101119n · Cited by 269 · see citing works
Jitter and shimmer measurements for speaker recognition
Farrús, M., Hernando, J., & Ejarque, P. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-147 · Cited by 221 · see citing works
Temporal parameters of spontaneous speech in Alzheimer's disease
Hoffmann, I., Németh, D., Dye, C. D., Pákáski, M., Irinyi, T., & Kálmán, J. (2009) · International Journal of Speech-Language Pathology · doi:10.3109/17549500903137256 · Cited by 180 · see citing works
Harmonics-to-Noise Ratio and Psychophysical Measurement of the Degree of Hoarseness
Yumoto, E., Sasaki, Y., & Okamura, H. (1984) · Journal of Speech Language and Hearing Research · doi:10.1044/jshr.2701.02 · Cited by 173 · see citing works
López-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
Blanken, G., Dittmann, J., Haas, J.-C., & Wallesch, C. (1987) · Cognition · doi:10.1016/s0010-0277(87)80011-2 · Cited by 114 · see citing works
Tiedt, H. O., Weber, J. E., Pauls, A., Beier, K. M., & Lueschow, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069107 · Cited by 61 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A comprehensive survey on feature selection in the various fields of machine learning
Dhal, P., & Azad, C. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02550-9 · Cited by 502 · see citing works
Tubishat, M., Idris, N. B., Shuib, L. M., Abushariah, M. A. M., & Mirjalili, S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113122 · Cited by 377 · see citing works
García, S. D. L. F., Ritchie, C. W., & Luz, S. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06047 · Cited by 265 · see citing works
A systematic literature review of automatic Alzheimer’s disease detection from speech and language
Petti, U., Baker, S., & Korhonen, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa174 · Cited by 168 · see citing works
Alzheimer's disease and automatic speech analysis: A review
Pulido, M. L. B., Hernández, J. B. A., Ballester, M. Á. F., González, C. M. T., Mekyska, J., & Smékal, Z. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113213 · Cited by 120 · see citing works
Speech- and language-based classification of Alzheimer's disease: a systematic review
Vigo, I., Coelho, L., & Reis, S. (2022) · Bioengineering · doi:10.3390/bioengineering9010027 · Cited by 83 · see citing works
Idrisoglu, A., Dallora, A. L., Anderberg, P., & Berglund, J. S. (2023) · Journal of Medical Internet Research · doi:10.2196/46105 · Cited by 83 · see citing works
Harris, C., Tang, Y., Birnbaum, E., Cherian, C., Mendhe, D., & Chen, M. H. (2024) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acae016 · Cited by 81 · see citing works
Fröhlich, H., Bontridder, N., Petrovska‐Delacrétaz, D., Glaab, E., Kluge, F., El‐Yacoubi, M. A., Valero, M. M., Corvol, J., Eskofier, B. M., Gyseghem, J.-M. V., et al. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.788427 · Cited by 67 · see citing works
Self-citationMahendran, N., Vincent, P. M. D. R., Srinivasan, K., & Chang, C. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.784814 · Cited by 55 · see citing works
Zolnoori, M., Zolnour, A., & Topaz, M. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102624 · Cited by 41 · see citing works
Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review
Qi, X., Zhou, Q., Dong, J., & Bao, W. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1224723 · Cited by 42 · see citing works
The Application of Artificial Intelligence in Alzheimer's Research
Zhao, Q., Xu, H., Li, J., Akhtar, F., & Qiao, L. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010037 · Cited by 32 · see citing works
Soroski, T., Vasco, T. D. C., Newton‐Mason, S. M., Granby, S., Lewis, C., Harisinghani, A., Rizzo, M., Conati, C., Murray, G., Carenini, G., et al. (2022) · JMIR Aging · doi:10.2196/33460 · Cited by 25 · see citing works
Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly
Ahn, K., Cho, M., Kim, S., Lee, K. E., Song, Y., Yoo, S., Jeon, S. Y., Kim, J. L., Yoon, D. H., & Kong, H. (2023) · Bioengineering · doi:10.3390/bioengineering10091093 · Cited by 21 · see citing works
Al-Hammadi, M., Fleyeh, H. A., Åberg, A. C., Halvorsen, K., & Thomas, I. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231459 · Cited by 23 · see citing works
Nasrolahzadeh, M., Haddadnia, J., & Rahnamayan, S. (2020) · IEEE Access · doi:10.1109/access.2020.3001426 · Cited by 18 · see citing works
Acoustic and prosodic information for home monitoring of bipolar disorder
Farrús, M., Codina, J., & Escudero, J. (2021) · Health Informatics Journal · doi:10.1177/1460458220972755 · Cited by 18 · see citing works
Silva, U. D., Madanian, S., Olsen, S., Templeton, J. M., Poellabauer, C., Schneider, S. L., Narayanan, A., & Rubaiat, R. (2025) · Journal of Medical Internet Research · doi:10.2196/63004 · Cited by 18 · see citing works
Gong, C., Qin, H., & El‐Yacoubi, M. A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3585379 · Cited by 13 · see citing works
Self-citationScoping review of the relationship between diabetes and voice quality
Saghiri, M. A., Vakhnovetsky, A., & Vakhnovetsky, J. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109782 · Cited by 12 · see citing works
Kálmán, J., Devanand, D. P., Gosztolya, G., Balogh, R., Imre, N., Tóth, L., Hoffmann, I., Kovács, I., Vincze, V., & Pákáski, M. (2022) · Current Alzheimer Research · doi:10.2174/1567205019666220418155130 · Cited by 12 · see citing works
A Survey of Different Machine Learning Models for Alzheimer Disease Prediction
Davuluri, R. (2020) · International Journal of Emerging Trends in Engineering Research · doi:10.30534/ijeter/2020/73872020 · Cited by 11 · see citing works
Assessing the Interpretability of Machine Learning Models in Early Detection of Alzheimer's Disease
Haddada, K., Khedher, M. I., Jemai, O., Khedher, S. I., & El-Yacoubi, M. A. (2024) · International Conference on Human System Interaction · doi:10.1109/hsi61632.2024.10613551 · Cited by 6 · see citing works
Self-citationA Comparison of Speech Features between Mild Cognitive Impairment and Healthy Aging Groups
Kim, K. W., Na, S., Chung, Y., & Shin, B. (2021) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2021.20.4.52 · Cited by 9 · see citing works
Boualoulou, N., Nsiri, B., Drissi, T. B., & Zayrit, S. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224301019 · Cited by 8 · see citing works
Exploring the impact of type II diabetes mellitus on voice quality
Saghiri, M. A., Vakhnovetsky, J., Amanabi, M., Karamifar, K., Farhadi, M., Amini, S. B., & Conte, M. (2024) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-024-08485-4 · Cited by 6 · see citing works
Detection of Alzheimer’s Disease Through Speech Features and Machine Learning Classifiers
Gulapalli, A. S., & Mittal, V. K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-6309-3_59 · Cited by 6 · see citing works
Combining Prosodic, Voice Quality and Lexical Features to Automatically Detect Alzheimer's DiseasePreprint
Farrús, M., & Codina, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09272 · Cited by 6 · see citing works
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
Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches
Kumar, R., & Azad, C. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19425-z · Cited by 5 · see citing works
The Application of Voice Biomarkers in Frailty: A Scoping Review
Wang, X., Han, S., Wang, W., Yang, Y., Zhang, W., Chen, S., Shan, S., Chen, M., & Wang, Z. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2024.12.029 · Cited by 5 · see citing works
Convolutional Speech Recognition with Pitch and Voice Quality FeaturesPreprint
Cámbara, G., Luque, J., & Farrús, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.01309 · Cited by 4 · see citing works
Noninvasive Detection of Alzheimer’s Disease from Conversational Speech Using 1D-CNN
Alex, J. S. R., Bhyri, R., Usha, G. P., & Arvind, S. V. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8338-2_49 · Cited by 3 · see citing works
Influence of ASR and Language Model on Alzheimer's Disease DetectionPreprint
Codina, J., Cámbara, G., Luque, J., & Farrús, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15704 · Cited by 2 · see citing works
Ahmadian, R., Ghatee, M., & Wahlström, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4616995 · Cited by 2 · see citing works
Layer-wise analysis of Wav2Vec for early detection of cognitive decline
Talasila, S. L., & Rodda, V. (2026) · International Journal of Speech Technology · doi:10.1007/s10772-026-10253-0 · Cited by 1 · see citing works
Sahwal, A. K., Singh, M., Kumari, N., Murari, K., & Prabha, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37303-9_15 · Cited by 1 · see citing works
Exploring the Efficacy of Text Embeddings in Early Dementia Diagnosis from Speech
Ajroudi, K., Khedher, M. I., Jemai, O., & El‐Yacoubi, M. A. (2024) · Conference on Human System Interactions · doi:10.1109/hsi61632.2024.10613581 · Cited by 1 · see citing works
Self-citationJones, S. A., Cosgrove, J., Wang, H., & Mathew, R. K. (2025) · medRxiv · doi:10.1101/2025.07.18.25331737 · Cited by 1 · see citing works
JETSAN: A Reference Workshop of E-Health in France
Istrate, D., & Frouin, F. (2018) · IRBM · doi:10.1016/j.irbm.2018.11.002
Soroski, T., Vasco, T. D. C., Newton‐Mason, S. M., Granby, S., Lewis, C., Harisinghani, A., Rizzo, M., Conati, C., Murray, G., Carenini, G., et al. (2021) · doi:10.2196/preprints.33460
Voice Quality and Pitch Features in Transformer-Based Speech Recognition
Cámbara, G., Farrús, M., & Luque, J. (2022) · Speech prosody · doi:10.21437/speechprosody.2022-113
Survey of Identification of Alzheimer’s Disease Using MRI, Speech and MMSE
Bhanusree, Y., Bulusu, D., Chinni, D., Narahari, A., Simhadri, S. S., & Bommareddy, V. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-4990-6_23
การวเคราะหคณลกษณะของเสยงเพอทำนายคะแนนแอตเทนชนในแบบประเมนโมคา
ตรีมงคลโชค, ว. (2022) · doi:10.58837/chula.the.2022.781
Comprehensive Dashboard for Alzheimer’s Disease Through Machine Learning
Narayan, S. S., & Annapurna, V. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-5974-7_49
PredictStr: A Balanced Benchmark Dataset for Improve Stroke Prediction
Romdhane, T. F., Khedher, M. I., & El‐Yacoubi, M. A. (2024) · Conference on Human System Interactions · doi:10.1109/hsi61632.2024.10613533
Self-citationMulti-Task Learning with Acoustic Features for Alzheimer’s Disease Detection
Sviderski, M., Barakat, B., Allen, B., & MacFarlane, K. (2024) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac61394.2024.10718774
Türküsay, S., Oturakçı, M., Ekinci, E., & Eliiyi, D. T. (2025) · Middle East Journal of Science · doi:10.51477/mejs.1605943
Tang, J., Wang, X., Zhu, W., Yang, W., Yao, X., Wang, Y., Zhang, X., Wang, F., & Yang, J. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120976
Spens, I., Zhang, Z., Azadmaleki, H., Vergez, S., Flaherty, G., Onorato, N., Noble, J. M., McDonald, M., & Zolnoori, M. (2026) · JMIR Formative Research · doi:10.2196/87295
- Authors
- Mirzaei, S., El Yacoubi, M., Garcia-Salicetti, S., Boudy, J., Kahindo, C., Cristancho-Lacroix, V., Kerhervé, H., & Rigaud, A.-S.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Hôpital Cochin (FR)
- Université Paris Descartes (FR)
- Corresponding author
- Mirzaei, S.
- ORCID
- Venue
- IRBM
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 39
- Issue
- 6
- Pages
- 430-435
- ISSN
- 1876-0988 (ISSN-L)
- 1959-0318
- OpenAlex
- W2895891552
- Cited by
- 52
- References
- 18
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 10 · 2022: 10 · 2021: 6 · 2020: 8 · 2019: 1 · 2018: 1
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IRBM (publishedVersion)
- HAL (submittedVersion)
- Funders
- Fondation MAIF
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, early Alzheimer's disease, mild cognitive impairment, voice analysis, speech features, two-stage feature selection, wrapper-based feature selection, jitter, harmonic-to-noise ratio, read speech, non-invasive disease detection, acoustic features
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
