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

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.

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Publication Details

Journal
Algorithms
Published
2024-11-15
DOI
https://doi.org/10.3390/a17110527
Citations
1
Primary Topic
Obstructive Sleep Apnea Research
Type
article
Field-Weighted Citation Impact
0.33

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article

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

Felipe D. J. Sorcia‐Vázquez, Jesús E. Valdez-Reséndiz, Juan Carlos Mixteco-Sánchez, Erasmo Misael Rentería-Vargas et al.
1 citations
Algorithms
Obstructive Sleep Apnea Research
0.33
article

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

Felipe D. J. Sorcia‐Vázquez, Jesús E. Valdez-Reséndiz, Juan Carlos Mixteco-Sánchez, Erasmo Misael Rentería-Vargas, Moises Ramos-Martinez, Jesse Yoe Rumbo Morales, Gerardo Ortíz-Torres, Mario Martínez García, Mayra G. Mena-Enriquez, Estela Sarmiento-Bustos
article en
1 citations

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.

AlgorithmsVol. 17(11)
Universidad de Guadalajara (MX), Universidad Tecnológica Emiliano Zapata del Estado de Morelos, Technological University of Mexico (MX), Tecnológico de Monterrey (MX)
Universidad de Guadalajara
Peace, Justice and strong institutions
Openalex Percentile: Top 36%
Obstructive Sleep Apnea Research
0.33
DOIOpen Access PDFOpenAlex
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