Extracting heart rate variability from nirs signals for an explainable detection of learning disorders
Artificial Intelligence (AI) has improved our ability to process large amounts of data. These tools are particularly interesting in medical contexts because they evaluate the variables from patients’ screening evaluation and disentangle the information that they contain. In this study, we propose a...
- Autores:
-
Arco, Juan E.
Gallego-Molina, Nicolás J.
López-Pérez, Pedro J.
Ramírez, Javier
Górriz, Juan M.
Ortiz, Andrés
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13820
- Acceso en línea:
- https://hdl.handle.net/11323/13820
https://repositorio.cuc.edu.co/
- Palabra clave:
- Dyslexia
Explicability
Heart rate variability
Machine learning
NIRS
Signal processing
- Rights
- closedAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
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Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| title |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| spellingShingle |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders Dyslexia Explicability Heart rate variability Machine learning NIRS Signal processing |
| title_short |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| title_full |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| title_fullStr |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| title_full_unstemmed |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| title_sort |
Extracting heart rate variability from nirs signals for an explainable detection of learning disorders |
| dc.creator.fl_str_mv |
Arco, Juan E. Gallego-Molina, Nicolás J. López-Pérez, Pedro J. Ramírez, Javier Górriz, Juan M. Ortiz, Andrés |
| dc.contributor.author.none.fl_str_mv |
Arco, Juan E. Gallego-Molina, Nicolás J. López-Pérez, Pedro J. Ramírez, Javier Górriz, Juan M. Ortiz, Andrés |
| dc.subject.proposal.eng.fl_str_mv |
Dyslexia Explicability Heart rate variability Machine learning NIRS Signal processing |
| topic |
Dyslexia Explicability Heart rate variability Machine learning NIRS Signal processing |
| description |
Artificial Intelligence (AI) has improved our ability to process large amounts of data. These tools are particularly interesting in medical contexts because they evaluate the variables from patients’ screening evaluation and disentangle the information that they contain. In this study, we propose a novel method for detecting developmental dyslexia by extracting heart signals from NIRS. Features in terms of different domains based on heart rate variability (HRV) are computed from the extracted signal, and dimensionality of the resulting data is reduced through Principal Component Analysis (PCA). To evaluate the discriminability of the information patterns associated with normal controls and dyslexic patients, the resulting components are entered into a linear classifier to evaluate the discriminability of the information patterns associated with normal controls and dyslexic patients, leading to an area under the ROC curve of 0.79. The explanatory nature of our framework, based on Shapley Additive Explanations (SHAP), yields a deeper understanding of the evaluated phenomenon, revealing the presence of behavioral variables highly correlated with the model’s features. These findings demonstrate that heart information can be extracted from a different equipment than electrocardiogram tools, and that cardiac signal variables can be used to detect dyslexia in an early stage. |
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2024 |
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2024-11-25T17:53:42Z |
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2024-11-25T17:53:42Z 2025-05-31 |
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2024-05-31 |
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Capítulo - Parte de Libro |
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Arco, J.E., Gallego-Molina, N.J., López-Pérez, P.J., Ramírez, J., Górriz, J.M., Ortiz, A. (2024). Extracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_12 |
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978-3-031-61139-1 |
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https://hdl.handle.net/11323/13820 |
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10.1007/978-3-031-61140-7_12 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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978-3-031-61140-7 |
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Arco, J.E., Gallego-Molina, N.J., López-Pérez, P.J., Ramírez, J., Górriz, J.M., Ortiz, A. (2024). Extracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_12 978-3-031-61139-1 10.1007/978-3-031-61140-7_12 Corporación Universidad de la Costa REDICUC - Repositorio CUC 978-3-031-61140-7 |
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https://hdl.handle.net/11323/13820 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Lecture notes in computer science |
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Artificial intelligence for neuroscience and emotional systems |
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Arco, J.E., Gallego-Molina, N.J., Ortiz, A., Arroyo-Alvis, K., López-Pérez, P.J. Identifying HRV patterns in ECG signals as early markers of dementia (2024) Expert Syst. Appl., 243. Arco, J.E., Ortiz, A., Castillo-Barnes, D., Górriz, J.M., Ramírez, J. Quantifying inter-hemispheric differences in Parkinson’s disease using siamese networks (2022) Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., De La Paz López, F., Adeli, H. (Eds.) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, Pp. 156–165, Arco, J.E., Ortiz, A., Ramírez, J., Zhang, Y.D., Górriz, J.M. Tiled sparse coding in eigenspaces for image classification (2022) Int. J. Neural Syst., 32 (3). Arco, J.E., Ortiz, A., Castillo-Barnes, D., Górriz, J.M., Ramírez, J. Ensembling shallow Siamese architectures to assess functional asymmetry in Alzheimer’s disease progression (2023) Appl. Soft Comput., 134. Arco, J.E., Ortiz, A., Gallego-Molina, N.J., Górriz, J.M., Ramírez, J. Enhancing multimodal patterns in neuroimaging by Siamese neural networks with self-attention mechanism (2023) Int. J. Neural Syst., 33 (4). Arco, J.E. Probabilistic combination of non-linear eigenprojections for ensemble classification (2022) IEEE Trans. Emerg. Top. Comput. Intell., 7, pp. 1-11. Arco, J.E., Ramírez, J., Puntonet, C.G., Górriz, J.M., Ruz, M. Improving short-term prediction from MCI to AD by applying Searchlight analysis (2016) 2016 IEEE 13Th International Symposium on Biomedical Imaging (ISBI), pp. 10- 13. de Vos, A., Vanvooren, S., Vanderauwera, J., Ghesqui Ère, P., Wouters, J. A longitudinal study investigating neural processing of speech envelope modulation rates in children with (a family risk for) dyslexia (2017) Cortex, 93, pp. 206-219. Dutt, S. Comparison of classification methods used in machine learning for dysgraphia identification (2021) Turk. J. Comput. Math. Educ. (Turcomat), 12, pp. 1886-1891. Fishburn, F.A., Ludlum, R.S., Vaidya, C.J., Medvedev, A.V. Temporal derivative distribution repair (TDDR): A motion correction method for FNIRS (2019) Neuroimage, 184, pp. 171-179. Fleming, S. Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: A systematic review of observational studies (2011) Lancet, 377 (9770), pp. 1011-1018. Frattola, A. Time and frequency domain estimates of spontaneous baroreflex sensitivity provide early detection of autonomic dysfunction in diabetes mellitus (1997) Diabetologia, 40, pp. 1470-1475. Gallego-Molina, N.J., Ortiz, A., Martínez-Murcia, F.J., Rodríguez-Rodríguez, I., Luque, J.L. Assessing functional brain network dynamics in dyslexia from FNIRS data (2023) Int. J. Neural Syst., 33 (4). Golland, P., Fischl, B. Permutation Tests for Classification: Towards Statistical Significance in Image-Based Studies, Taylor, C., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 330–341. Springer, Heidelberg (2003). https://doi.org/10. 1007/978-3-540-45087-0_28 Graham, G., Csicsery, N., Stasiowski, E., Thouvenin, G., Mather, W. Genome-scale transcriptional dynamics and environmental biosensing (2020) Proc. Natl. Acad. Sci., 117, pp. 3301-3306. Górriz, J. Computational approaches to explainable artificial intelligence: advances in theory, applications and trends (2023) Inf. Fus., 100. Górriz, J.M. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications (2020) Neurocomputing, 410, pp. 237-270. Jiménez-Mesa, C., Arco, J.E., Valentí-Soler, M. Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern (2023) Int. J. Neural Syst., 33 (4). Lotufo, P., Valiengo, L., Benseñor, I., Brunoni, A. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs (2012) Epilepsia, 53, pp. 272-282. Ortiz, A., Martinez-Murcia, F.J., Luque, J.L., Giménez, A., Morales-Ortega, R., Ortega, J. Dyslexia diagnosis by EEG temporal and spectral descriptors: An anomaly detection approach (2020) Int. J. Neural Syst., 30 (7). Pollonini, L., Olds, C., Abaya, H., Bortfeld, H., Beauchamp, M.S., Oghalai, J.S. Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy (2014) Hear. Res., 309, pp. 84-93. Sieciński, S., Kostka, P., Tkacz, E. Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms on healthy volunteers (2020) Sensors, 20, p. 4522. Wu, X. Optimal quantization by matrix searching (1991) J. Algorithms, 12 (4), pp. 663-673. |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AGhttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbArco, Juan E.Gallego-Molina, Nicolás J.López-Pérez, Pedro J.Ramírez, JavierGórriz, Juan M.Ortiz, Andrés2024-11-25T17:53:42Z2025-05-312024-11-25T17:53:42Z2024-05-31Arco, J.E., Gallego-Molina, N.J., López-Pérez, P.J., Ramírez, J., Górriz, J.M., Ortiz, A. (2024). Extracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_12978-3-031-61139-1https://hdl.handle.net/11323/1382010.1007/978-3-031-61140-7_12Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/978-3-031-61140-7Artificial Intelligence (AI) has improved our ability to process large amounts of data. These tools are particularly interesting in medical contexts because they evaluate the variables from patients’ screening evaluation and disentangle the information that they contain. In this study, we propose a novel method for detecting developmental dyslexia by extracting heart signals from NIRS. Features in terms of different domains based on heart rate variability (HRV) are computed from the extracted signal, and dimensionality of the resulting data is reduced through Principal Component Analysis (PCA). To evaluate the discriminability of the information patterns associated with normal controls and dyslexic patients, the resulting components are entered into a linear classifier to evaluate the discriminability of the information patterns associated with normal controls and dyslexic patients, leading to an area under the ROC curve of 0.79. The explanatory nature of our framework, based on Shapley Additive Explanations (SHAP), yields a deeper understanding of the evaluated phenomenon, revealing the presence of behavioral variables highly correlated with the model’s features. These findings demonstrate that heart information can be extracted from a different equipment than electrocardiogram tools, and that cardiac signal variables can be used to detect dyslexia in an early stage.9 páginasapplication/pdfengSpringer VerlagGermanyLecture notes in computer scienceArtificial intelligence for neuroscience and emotional systemsArco, J.E., Gallego-Molina, N.J., Ortiz, A., Arroyo-Alvis, K., López-Pérez, P.J. Identifying HRV patterns in ECG signals as early markers of dementia (2024) Expert Syst. Appl., 243.Arco, J.E., Ortiz, A., Castillo-Barnes, D., Górriz, J.M., Ramírez, J. Quantifying inter-hemispheric differences in Parkinson’s disease using siamese networks (2022) Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., De La Paz López, F., Adeli, H. (Eds.) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, Pp. 156–165,Arco, J.E., Ortiz, A., Ramírez, J., Zhang, Y.D., Górriz, J.M. Tiled sparse coding in eigenspaces for image classification (2022) Int. J. Neural Syst., 32 (3).Arco, J.E., Ortiz, A., Castillo-Barnes, D., Górriz, J.M., Ramírez, J. Ensembling shallow Siamese architectures to assess functional asymmetry in Alzheimer’s disease progression (2023) Appl. Soft Comput., 134.Arco, J.E., Ortiz, A., Gallego-Molina, N.J., Górriz, J.M., Ramírez, J. Enhancing multimodal patterns in neuroimaging by Siamese neural networks with self-attention mechanism (2023) Int. J. Neural Syst., 33 (4).Arco, J.E. Probabilistic combination of non-linear eigenprojections for ensemble classification (2022) IEEE Trans. Emerg. Top. Comput. Intell., 7, pp. 1-11.Arco, J.E., Ramírez, J., Puntonet, C.G., Górriz, J.M., Ruz, M. Improving short-term prediction from MCI to AD by applying Searchlight analysis (2016) 2016 IEEE 13Th International Symposium on Biomedical Imaging (ISBI), pp. 10- 13.de Vos, A., Vanvooren, S., Vanderauwera, J., Ghesqui Ère, P., Wouters, J. A longitudinal study investigating neural processing of speech envelope modulation rates in children with (a family risk for) dyslexia (2017) Cortex, 93, pp. 206-219.Dutt, S. Comparison of classification methods used in machine learning for dysgraphia identification (2021) Turk. J. Comput. Math. Educ. (Turcomat), 12, pp. 1886-1891.Fishburn, F.A., Ludlum, R.S., Vaidya, C.J., Medvedev, A.V. Temporal derivative distribution repair (TDDR): A motion correction method for FNIRS (2019) Neuroimage, 184, pp. 171-179.Fleming, S. Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: A systematic review of observational studies (2011) Lancet, 377 (9770), pp. 1011-1018.Frattola, A. Time and frequency domain estimates of spontaneous baroreflex sensitivity provide early detection of autonomic dysfunction in diabetes mellitus (1997) Diabetologia, 40, pp. 1470-1475.Gallego-Molina, N.J., Ortiz, A., Martínez-Murcia, F.J., Rodríguez-Rodríguez, I., Luque, J.L. Assessing functional brain network dynamics in dyslexia from FNIRS data (2023) Int. J. Neural Syst., 33 (4).Golland, P., Fischl, B. Permutation Tests for Classification: Towards Statistical Significance in Image-Based Studies, Taylor, C., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 330–341. Springer, Heidelberg (2003). https://doi.org/10. 1007/978-3-540-45087-0_28Graham, G., Csicsery, N., Stasiowski, E., Thouvenin, G., Mather, W. Genome-scale transcriptional dynamics and environmental biosensing (2020) Proc. Natl. Acad. Sci., 117, pp. 3301-3306.Górriz, J. Computational approaches to explainable artificial intelligence: advances in theory, applications and trends (2023) Inf. Fus., 100.Górriz, J.M. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications (2020) Neurocomputing, 410, pp. 237-270.Jiménez-Mesa, C., Arco, J.E., Valentí-Soler, M. Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern (2023) Int. J. Neural Syst., 33 (4).Lotufo, P., Valiengo, L., Benseñor, I., Brunoni, A. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs (2012) Epilepsia, 53, pp. 272-282.Ortiz, A., Martinez-Murcia, F.J., Luque, J.L., Giménez, A., Morales-Ortega, R., Ortega, J. Dyslexia diagnosis by EEG temporal and spectral descriptors: An anomaly detection approach (2020) Int. J. Neural Syst., 30 (7).Pollonini, L., Olds, C., Abaya, H., Bortfeld, H., Beauchamp, M.S., Oghalai, J.S. Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy (2014) Hear. Res., 309, pp. 84-93.Sieciński, S., Kostka, P., Tkacz, E. Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms on healthy volunteers (2020) Sensors, 20, p. 4522.Wu, X. Optimal quantization by matrix searching (1991) J. Algorithms, 12 (4), pp. 663-673.127118https://link.springer.com/chapter/10.1007/978-3-031-61140-7_12Extracting heart rate variability from nirs signals for an explainable detection of learning disordersCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceDyslexiaExplicabilityHeart rate variabilityMachine learningNIRSSignal processingPublicationORIGINALExtracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders.pdfExtracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders.pdfapplication/pdf155295https://repositorio.cuc.edu.co/bitstreams/d72f6ced-ec02-4737-805e-a93a25584004/downloadb0ed653f4f5b3915e3cd1af0e2d47ddaMD51LICENSElicense.txtlicense.txttext/plain; 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04:01:05.577https://creativecommons.org/licenses/by-nc-sa/4.0/© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AGopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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