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 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.

• 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)