{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T22:59:24Z","timestamp":1793141964427,"version":"4.5.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Manipal"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Parkinson\u2019s disease affects both motor and non-motor functions, including vocal features that may indicate underlying mental health conditions such as depression. This work proposes a novel framework for simulated depression risk classification using vocal biomarkers derived from the UCI Parkinson\u2019s dataset. A Self-Attention-Enhanced Multilayer Perceptron-MLP architecture is used model interactions between key acoustic features, particularly Harmonic-to-Noise Ratio and Jitter, which serve as the basis for generating binary depression risk labels. The proposed model outperforming traditional and deep learning benchmarks including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), TabNet, CNN-LSTM, Deep Neural Network (DNN), and Explainable Boosting Machine (EBM) with an accuracy of 97%, F1-score of 98%, recall of 95%, and specificity of 100%, While EBM offers strong interpretability, the attention-enhanced model demonstrates optimal predictive capability. These findings highlight the efficacy of voice-based features combined with attention mechanisms for early, non-invasive identification of depression risk in PD patients.<\/jats:p>","DOI":"10.1038\/s41598-026-37773-8","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T02:05:12Z","timestamp":1770602712000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Simulated depression risk classification from Parkinson\u2019s voice features using a self-attention-enhanced MLP architecture"],"prefix":"10.1038","volume":"16","author":[{"given":"Nalineekumari","family":"Arasavali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed.","family":"Ashik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaddadi","family":"Nirmal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mogadala Vinod","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"U","family":"Siddaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"issue":"6","key":"37773_CR1","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s40266-022-00942-1","volume":"39","author":"S Prange","year":"2022","unstructured":"Prange, S., Klinger, H., Laurencin, C., Danaila, T. & Thobois, S. Depression in patients with parkinson\u2019s disease: current Understanding of its neurobiology and implications for treatment. Drugs Aging. 39 (6), 417\u2013439 (2022).","journal-title":"Drugs Aging"},{"key":"37773_CR2","doi-asserted-by":"crossref","unstructured":"Wright, H. & Aharonson, V. Vocal Feature Changes for Monitoring Parkinson\u2019s Disease Progression\u2014A Systematic Review. Brain Sciences, 15(3), 320 (2025).","DOI":"10.3390\/brainsci15030320"},{"issue":"2","key":"37773_CR3","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1002\/mds.24025","volume":"27","author":"J P\u00e9ron","year":"2012","unstructured":"P\u00e9ron, J., Dondaine, T., Le Jeune, F., Grandjean, D. & V\u00e9rin, M. Emotional processing in parkinson\u2019s disease: a systematic review. Mov. Disord. 27 (2), 186\u2013199 (2012).","journal-title":"Mov. Disord."},{"key":"37773_CR4","doi-asserted-by":"crossref","unstructured":"Azadi, H., Akbarzadeh-T, M. R., Shoeibi, A. & Kobravi, H. R. Evaluating the effect of Parkinson\u2019s disease on jitter and shimmer speech features. Advanced Biomedical Research, 10(1), 54 (2021).","DOI":"10.4103\/abr.abr_254_21"},{"issue":"2","key":"37773_CR5","first-page":"199","volume":"21","author":"JC Saldanha","year":"2023","unstructured":"Saldanha, J. C., Suvarna, M., Satish, D. & Santhmayor, C. Jitter as a quantitative indicator of dysphonia in parkinson\u2019s disease. Int. J. Intell. Syst. Technol. Appl. 21 (2), 199\u2013228 (2023).","journal-title":"Int. J. Intell. Syst. Technol. Appl."},{"key":"37773_CR6","unstructured":"Schoentgen, J., Kacha, A. & Grenez, F. Joint analysis of vocal jitter, flutter and tremor in vowels sustained by normophonic and parkinson speakers. Proceedings E Report, 37, (2019)."},{"issue":"6","key":"37773_CR7","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1002\/mds.21940","volume":"23","author":"J M\u00f6bes","year":"2008","unstructured":"M\u00f6bes, J., Joppich, G., Stiebritz, F., Dengler, R. & Schr\u00f6der, C. Emotional speech in parkinson\u2019s disease. Mov. Disord. 23 (6), 824\u2013829 (2008).","journal-title":"Mov. Disord."},{"key":"37773_CR8","doi-asserted-by":"crossref","unstructured":"Silva, L. F., Gama, A. C. C., Cardoso, F. E. C., Reis, C. A. D. C. & Bassi, I. B. Idiopathic Parkinson\u2019s disease: vocal and quality of life analysis. Arquivos de neuro-psiquiatria, 70, 674\u2013679 (2012).","DOI":"10.1590\/S0004-282X2012000900005"},{"key":"37773_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocn.2019.12.029","volume":"72","author":"A Ma","year":"2020","unstructured":"Ma, A., Lau, K. K. & Thyagarajan, D. Voice changes in parkinson\u2019s disease: what are they telling us? J. Clin. Neurosci. 72, 1\u20137 (2020).","journal-title":"J. Clin. Neurosci."},{"key":"37773_CR10","doi-asserted-by":"crossref","unstructured":"Hassani, R. & Manjunath, C. R. October. Predicting Parkinson\u2019s Disease Using Different Features Based on Xgboost of Voice Data. In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) 496\u2013502 (IEEE, 2022).","DOI":"10.1109\/ICTACS56270.2022.9988089"},{"key":"37773_CR11","doi-asserted-by":"crossref","unstructured":"Bechir, M. K. & Atasoy, F. Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach. Gazi \u00dcniversitesi Fen Bilimleri Dergisi Part C: Tasar\u0131m ve Teknoloji, 1\u20131 (2025).","DOI":"10.29109\/gujsc.1489959"},{"key":"37773_CR12","doi-asserted-by":"crossref","unstructured":"Nissar, I., Mir, W. A. & Shaikh, T. A. March. Machine learning approaches for detection and diagnosis of Parkinson\u2019s disease-a review. In 2021 7th international conference on advanced computing and communication systems (ICACCS) 1, 898\u2013905 (IEEE, 2021).","DOI":"10.1109\/ICACCS51430.2021.9441885"},{"key":"37773_CR13","doi-asserted-by":"crossref","unstructured":"Yadav, S. & Singh, M. K. Hybrid machine learning classifier and ensemble techniques to detect Parkinson\u2019s disease patients. SN Computer Science, 2(3),189 (2021).","DOI":"10.1007\/s42979-021-00587-8"},{"issue":"3","key":"37773_CR14","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1080\/02648725.2023.2200333","volume":"40","author":"K Li","year":"2024","unstructured":"Li, K. et al. Parkinson\u2019s disease detection and classification using EEG based on deep CNN-LSTM model. Biotechnol. Genet. Eng. Rev. 40 (3), 2577\u20132596 (2024).","journal-title":"Biotechnol. Genet. Eng. Rev."},{"key":"37773_CR15","doi-asserted-by":"crossref","unstructured":"Muntaha, S., Dewanjee, S. & Uddin, M. N. December. Vocal Features-Driven Parkinson\u2019s Identification through Attention-Based LSTM-BiGRU with LOPO CV and EBM. In 2024 27th International Conference on Computer and Information Technology (ICCIT) 1305\u20131310 (IEEE, 2024).","DOI":"10.1109\/ICCIT64611.2024.11021796"},{"key":"37773_CR16","doi-asserted-by":"crossref","unstructured":"Loh, H. W. et al. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011\u20132022). Computer methods and programs in biomedicine, 226, 107161 (2022).","DOI":"10.1016\/j.cmpb.2022.107161"},{"issue":"1","key":"37773_CR17","first-page":"e70082","volume":"17","author":"A Shakeri","year":"2025","unstructured":"Shakeri, A. & Farmanbar, M. Natural Language processing in alzheimer\u2019s disease research: systematic review of methods, data, and efficacy. Alzheimer\u2019s Dementia: Diagnosis Assess. Disease Monit. 17 (1), e70082 (2025).","journal-title":"Alzheimer\u2019s Dementia: Diagnosis Assess. Disease Monit."},{"issue":"1","key":"37773_CR18","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TNSRE.2019.2946194","volume":"28","author":"Y Xia","year":"2019","unstructured":"Xia, Y., Yao, Z., Ye, Q. & Cheng, N. A dual-modal attention-enhanced deep learning network for quantification of parkinson\u2019s disease characteristics. IEEE Trans. Neural Syst. Rehabil. Eng. 28 (1), 42\u201351 (2019).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"37773_CR19","doi-asserted-by":"publisher","unstructured":"McCoy, B., Lawson, R. P. & Theeuwes, J. Attention and reinforcement learning in parkinson\u2019s disease. BioRxiv 2020.09.12.294702, 2020\u20132009. https:\/\/doi.org\/10.1101\/2020.09.12.294702 (2020).","DOI":"10.1101\/2020.09.12.294702"},{"key":"37773_CR20","doi-asserted-by":"crossref","unstructured":"Cuk, A. et al. Tuning attention based long-short term memory neural networks for Parkinson\u2019s disease detection using modified metaheuristics. Scientific Reports, 14(1), p.4309. (2024).","DOI":"10.1038\/s41598-024-54680-y"},{"key":"37773_CR21","doi-asserted-by":"crossref","unstructured":"Pei, G. et al. Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson\u2019s Disease (Cyborg and Bionic Systems, 2025).","DOI":"10.34133\/cbsystems.0293"},{"key":"37773_CR22","doi-asserted-by":"crossref","unstructured":"Zheng, S., Yao, X., Hao, Z. & Yang, Y. September. Application and Optimization of ResNet Model Based on Spatial Attention Mechanism for Early Diagnosis of Parkinson\u2019s Disease. In 2024 2nd International Conference on Machine Vision, Image Processing & Imaging Technology (MVIPIT) 118\u2013123 (IEEE, 2024).","DOI":"10.1109\/MVIPIT65697.2024.00028"},{"issue":"5","key":"37773_CR23","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/TBME.2012.2183367","volume":"59","author":"A Tsanas","year":"2012","unstructured":"Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J. & Ramig, L. O. Novel speech signal processing algorithms for high-accuracy classification of parkinson\u2019s disease. IEEE Trans. Biomed. Eng. 59 (5), 1264\u20131271. https:\/\/doi.org\/10.1109\/TBME.2012.2183367 (2012). Epub 2012 Jan 9. PMID: 22249592.","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"37773_CR24","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1016\/j.protcy.2014.10.138","volume":"16","author":"JP Teixeira","year":"2014","unstructured":"Teixeira, J. P. & Fernandes, P. O. Jitter, shimmer and HNR classification within gender, tones and vowels in healthy voices. Procedia Technol. 16, 1228\u20131237 (2014).","journal-title":"Procedia Technol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37773-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37773-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37773-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:53:58Z","timestamp":1781632438000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37773-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["37773"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-37773-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"assertion":[{"value":"25 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7869"}}