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Algorithms
Volume 17
Issue 11
10.3390/a17110527
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Article

Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques

by
Moises Ramos-Martinez
1,
Felipe D. J. Sorcia-Vázquez
1,
Gerardo Ortiz-Torres
1,
Mario Martínez García
1,
Mayra G. Mena-Enriquez
2,
Estela Sarmiento-Bustos
3,
Juan Carlos Mixteco-Sánchez
4,
Erasmo Misael Rentería-Vargas
1,
Jesús E. Valdez-Resendiz
5,* and
Jesse Yoe Rumbo-Morales
1
1
Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km.45.5, Ameca 46600, Jalisco, Mexico
2
Biomedical Sciences Department, University of Guadalajara, Tonalá 45425, Jalisco, Mexico
3
División Académica de Mecánica Industrial, Universidad Tecnológica Emiliano Zapata del Estado de Morelos, Av. Universidad Tecnológica No. 1, Col. Palo Escrito, Emiliano Zapata 62760, Morelos, Mexico
4
Natural and Exact Sciences Department, University of Guadalajara, Ameca 46600, Jalisco, Mexico
5
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, Mexico
*
Author to whom correspondence should be addressed.
Algorithms 2024, 17(11), 527; https://doi.org/10.3390/a17110527
Submission received: 10 October 2024 / Revised: 8 November 2024 / Accepted: 13 November 2024 / Published: 15 November 2024
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))
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Abstract

Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural network were implemented using the EPC model as inputs. ECG signals were preprocessed with a polynomial-based scheme to reduce noise, and the processed signals were transformed into a non-Gaussian physiological random field (NGPRF) for EPC model extraction from excursion sets. The classifiers were then applied to the EPC model inputs. Using the Apnea-ECG dataset, the proposed method achieved an accuracy of 98.5%, sensitivity of 94.5%, and specificity of 100%. Combining machine learning methods and geometrical features can effectively diagnose sleep apnea from single-lead ECG signals. The EPC model enhances clinical decision-making for evaluating this disease.
Keywords: sleep apnea; Euler characteristic; machine learning; random field sleep apnea; Euler characteristic; machine learning; random field

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MDPI and ACS Style

Ramos-Martinez, M.; Sorcia-Vázquez, F.D.J.; Ortiz-Torres, G.; Martínez García, M.; Mena-Enriquez, M.G.; Sarmiento-Bustos, E.; Mixteco-Sánchez, J.C.; Rentería-Vargas, E.M.; Valdez-Resendiz, J.E.; Rumbo-Morales, J.Y. Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques. Algorithms 2024, 17, 527. https://doi.org/10.3390/a17110527

AMA Style

Ramos-Martinez M, Sorcia-Vázquez FDJ, Ortiz-Torres G, Martínez García M, Mena-Enriquez MG, Sarmiento-Bustos E, Mixteco-Sánchez JC, Rentería-Vargas EM, Valdez-Resendiz JE, Rumbo-Morales JY. Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques. Algorithms. 2024; 17(11):527. https://doi.org/10.3390/a17110527

Chicago/Turabian Style

Ramos-Martinez, Moises, Felipe D. J. Sorcia-Vázquez, Gerardo Ortiz-Torres, Mario Martínez García, Mayra G. Mena-Enriquez, Estela Sarmiento-Bustos, Juan Carlos Mixteco-Sánchez, Erasmo Misael Rentería-Vargas, Jesús E. Valdez-Resendiz, and Jesse Yoe Rumbo-Morales. 2024. "Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques" Algorithms 17, no. 11: 527. https://doi.org/10.3390/a17110527

APA Style

Ramos-Martinez, M., Sorcia-Vázquez, F. D. J., Ortiz-Torres, G., Martínez García, M., Mena-Enriquez, M. G., Sarmiento-Bustos, E., Mixteco-Sánchez, J. C., Rentería-Vargas, E. M., Valdez-Resendiz, J. E., & Rumbo-Morales, J. Y. (2024). Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques. Algorithms, 17(11), 527. https://doi.org/10.3390/a17110527

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