Ponencia
Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals
Autor/es | Troncoso García, Ángela del Robledo
Martínez Ballesteros, María del Mar Martínez Álvarez, Francisco Troncoso Lora, Alicia |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2023-09 |
Fecha de depósito | 2024-04-11 |
Publicado en |
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ISBN/ISSN | 978-3-031-43084-8 978-3-031-43085-5 (online) |
Resumen | This paper explores the use of deep learning techniques for detecting sleep apnea. Sleep apnea is a common sleep disorder characterized
by abnormal breathing pauses or infrequent breathing during sleep. The current standard ... This paper explores the use of deep learning techniques for detecting sleep apnea. Sleep apnea is a common sleep disorder characterized by abnormal breathing pauses or infrequent breathing during sleep. The current standard for diagnosing sleep apnea involves overnight polysomnography, which is expensive and requires specialized equipment and personnel. The proposed method utilizes a neural network to analyze physiological signals, such as heart rate and respiratory patterns, that are recorded during sleep to authomatic sleep apnea detection. The neural network is trained on a dataset of polysomnography recordings to identify patterns that are indicative of sleep apnea. The results compare the use of different physiological signals to detect sleep apnea. Nasal airflow seems to have the most accurate results and higher specificity, whereas EEG and ECG have higher levels of sensitivity. The best model concerning accuracy is compared to bias models previously applied to sleep apnea detection in literature, achieving greater results. This approach has the potential to provide automatic sleep apnea detection, being an accessible solution for diagnosing sleep apnea. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | PID2020-117954RB-C21
TED2021-131311B-C22 |
Cita | Troncoso García, Á.d.R., Martínez Ballesteros, M.d.M., Martínez Álvarez, F. y Troncoso Lora, A. (2023). Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals. En Advances in Computational Intelligence (IWANN 2023) (626-637), Ponta Delgada (Portugal): Springer Link. |
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