| Autor: |
Moises Ramos-Martinez, 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, Jesse Yoe Rumbo-Morales |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Algorithms, Vol 17, Iss 11, p 527 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1999-4893 |
| DOI: |
10.3390/a17110527 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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