Classificação automática da doença de Alzheimer através de características extraídas de gravações de fala

Autores

  • Dieine Estela Bernieri Schiavon UFCSPA
  • Carla Diniz Lopes Becker UFCSPA

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1254

Palavras-chave:

Processamento de Fala, Aprendizagem Profunda, Doença de Alzheimer

Resumo

A doença de Alzheimer é uma patologia neurodegenerativa progressiva estando entre as formas mais comuns de demência em pessoas idosas. Alterações de memória são sintomas frequentes, e alterações de fala e linguagem podem ser sinais de declínio cognitivo. Os sistemas inteligentes têm potencial para uso como ferramentas de apoio ao diagnóstico. Objetivo: Propor um modelo de Rede Neural Convolucional para classificação da doença de Alzheimer utilizando características extraídas de gravações de fala. Método: Utilizamos segmentos de fala com e sem pausas de indivíduos saudáveis e com doença de Alzheimer para extrair características e reconhecer padrões em espectrogramas. Para o treinamento do modelo usamos validação cruzada estratificada de 5-folds. Resultados: Obtivemos métricas de acurácia, sensibilidade e especificidade de 97,37%, 97,04% e 97,62%, respectivamente. Conclusão: O modelo proposto apresentou resultados promissores podendo contribuir para o estudo de biomarcadores não invasivos, que detectem precocemente a doença de Alzheimer.

Biografias Autor

Dieine Estela Bernieri Schiavon, UFCSPA

Master’s Student, Federal University of Health Sciences of Porto Alegre – UFCSPA, Porto Alegre (RS), Brazil.

Carla Diniz Lopes Becker, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

Referências

World Health Organization. Dementia [Internet]. World Health Organization. 2023 [cited 2023 Dec 15]. Available from: https://www.who.int/news-room/fact-sheets/detail/dementia.

Guo Y, Li C, Roan C, Pakhomov S, Cohen T. Crossing the “Cookie Theft” Corpus Chasm: Applying What BERT Learns From Outside Data to the ADReSS Challenge Dementia Detection Task. Frontiers in Computer Science. 2021 Apr 16.

Yang A, Liu C, Wu J, Kou X, Shen R. A review on α-mangostin as a potential multi-targetdirected ligand for Alzheimer’s disease. European Journal of Pharmacology 2021 Ap;897:173950.

Mahajan P, Baths V. Acoustic and Language Based Deep Learning Approaches for Alzheimer’s Dementia Detection From Spontaneous Speech. Frontiers in Aging Neuroscience 2021 Feb 5; 13.

Haulcy R, Glass J. Classifying Alzheimer’s Disease Using Audio and Text-Based Representations of Speech. Frontiers in Psychology [Internet]. 2021 Jan 15 [cited 2023 Dec 15];11. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.624137.

Chlasta K, Wołk K. Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech. Frontiers in Psychology [Internet]. 2021 Feb 12 [cited 2023 Dec 15];11. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.623237.

Laguarta J, Subirana B. Longitudinal Speech Biomarkers for Automated Alzheimer’s Detection. Frontiers in Computer Science [Internet]. 2021 Apr 8 [cited 2023 Dec 15];3. Available from: https://www.frontiersin.org/articles/10.3389/fcomp.2021.624694.

de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review. Journal of Alzheimer’s Disease. 2020 Dec 8; 78(4):1547–74.

Yuan J, Cai X, Bian Y, Ye Z, Church K. Pauses for Detection of Alzheimer’s Disease. Frontiers in Computer Science [Internet]. 2021 Jan 29 [cited 2023 Dec 15];2. Available from: https://www.frontiersin.org/articles/10.3389/fcomp.2020.624488.

Alkenani AH, Li Y, Xu Y, Zhang Q. Predicting Prodromal Dementia Using Linguistic Patterns and Deficits. IEEE Access [Internet]. 2020 [cited 2023 Dec 15]; 8:193856–73. Available from: https://ieeexplore.ieee.org/abstract/document/9218925.

Pompili A, Abad A, de Matos DM, Martins IP. Pragmatic Aspects of Discourse Production for the Automatic Identification of Alzheimer’s Disease. IEEE Journal of Selected Topics in Signal Processing [Internet]. 2020 Feb 1;14(2):261–71. Available from: https://ieeexplore.ieee.org/document/8963723.

Liu Z, Guo Z, Ling Z, Li Y. Detecting Alzheimer’s Disease from Speech Using Neural Networks with Bottleneck Features and Data Augmentation [Internet]. IEEE Xplore. 2021. p. 7323–7. Available from: https://ieeexplore.ieee.org/document/9413566.

Gónzalez Atienza M, González López JA, Peinado AM. An Automatic System for Dementia Detection using Acoustic and Linguistic Features. digibugugres [Internet]. 2021 Jan 28; Available from: https://digibug.ugr.es/handle/10481/66645.

Bernieri G, Duarte JC. Identifying Alzheimer’s Disease Through Speech Using Emotion Recognition. Journal of Health Informatics [Internet]. 2023 Jul 20 [cited 2023 Dec 23];15(Especial). Available from: https://jhi.sbis.org.br/index.php/jhi-sbis/article/view/1093.

Becker JT. The Natural History of Alzheimer’s Disease. Archives of Neurology. 1994 Jun 1;51(6):585.

Luz S, Haider F, de la Fuente S, Fromm D, MacWhinney B. Alzheimer’s Dementia Recognition through Spontaneous Speech: The ADReSS Challenge. arXiv:200406833 [cs, eess, stat] [Internet]. 2020 Aug 5 [cited 2023 Dec 15]; Available from: https://arxiv.org/abs/2004.06833.

Luz S, Haider F, de la Fuente Garcia S, Fromm D, MacWhinney B. Editorial: Alzheimer’s Dementia Recognition through Spontaneous Speech. Frontiers in Computer Science [Internet]. 2021 Oct 21 [cited 2023 Dec 12];3. Available from: https://www.frontiersin.org/articles/10.3389/fcomp.2021.780169.

Goodglass H, Kaplan E, Weintraub S. BDAE: The Boston Diagnostic Aphasia Examination. Philadelphia, PA: Lippincott Williams & Wilkins; 2001. 2001.

Luz S, Haider F, de la Fuente S, Fromm D, MacWhinney B. Detecting cognitive decline using speech only: The ADReSSo Challenge. arXiv:210409356 [cs, eess] [Internet]. 2021 Mar 22 [cited 2023 Dec 23]; Available from: https://arxiv.org/abs/2104.09356.

Sainburg T. timsainb/noisereduce: v1.0. zenodoorg [Internet]. 2019 Jun 11 [cited 2023 Dec 23]; Available from: https://zenodo.org/record/3243139.

Steinmetz CJ, Reiss J. pyloudnorm: A simple yet flexible loudness meter in Python [Internet]. Audio Engineering Society Convention 150. Audio Engineering Society; 2021 [cited 2023 Dec 23]. Available from: https://csteinmetz1.github.io/pyloudnorm-eval/paper/pyloudnorm_preprint.pdf.

McFee B, Metsai A, McVicar M, Balke S, Thomé C, Raffel C, et al. librosa/librosa: 0.9.2 [Internet]. Zenodo. 2022. Available from: https://zenodo.org/record/6759664#.Y6n8x-zMK00.

Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks [Internet]. proceedings.mlr.press. PMLR; 2019 [cited 2023 Dec 23]. p. 6105–14. Available from: https://proceedings.mlr.press/v97/tan19a.html?ref=jina-ai-gmbh.ghost.io.

Team K. Keras documentation: Keras Applications [Internet]. keras.io. [cited 2023 Dec 23]. Available from: https://keras.io/api/applications.

Tammina S. Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images. International Journal of Scientific and Research Publications Volume 9, Issue 10, ISSN 2250-3153; 2019.

Publicado

2024-11-19

Como Citar

Schiavon, D. E. B., & Becker, C. D. L. (2024). Classificação automática da doença de Alzheimer através de características extraídas de gravações de fala. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1254

Artigos Similares

1 2 3 4 5 6 7 > >> 

Também poderá iniciar uma pesquisa avançada de similaridade para este artigo.

Artigos mais lidos do(s) mesmo(s) autor(es)