Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea
Obstructive sleep apnea (OSA) represents a significant health concern. While polysomnography (PSG) remains the gold standard, its resource-intensive nature has encouraged the exploration of further alternative approaches. Most of these were based on the heart rate variability (HRV) analysis, but onl...
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MDPI AG
2025-01-01
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| author | Daniele Padovano Arturo Martinez-Rodrigo José M. Pastor José J. Rieta Raul Alcaraz |
| author_facet | Daniele Padovano Arturo Martinez-Rodrigo José M. Pastor José J. Rieta Raul Alcaraz |
| author_sort | Daniele Padovano |
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| description | Obstructive sleep apnea (OSA) represents a significant health concern. While polysomnography (PSG) remains the gold standard, its resource-intensive nature has encouraged the exploration of further alternative approaches. Most of these were based on the heart rate variability (HRV) analysis, but only a few of them have presented a recurrence-based approach. The present paper addresses this gap by integrating convolutional neural networks (CNNs) with HRV recurrence analysis. Employing three different and publicly available databases from PhysioNet’s official repository (Apnea-ECG, MIT-BIH, and UCD-DB), the presented method was able to expose concealed patterns within the distance matrix of HRV’s phase space, which is discernible at an appropriate level of abstraction through CNNs. Under the challenging context of external validation (MIT-BIH and UCD for training, and Apnea-ECG for testing), the results obtained were comparable to those presented in the state of the art, achieving a peak accuracy of 75%, while maintaining balanced sensitivity and specificity at 74% and 75%, respectively. Moreover, these results obtained by the proposed CNN-based recurrence analysis of HRV also outperformed traditional time–frequency models, which have yielded values of accuracy lower than 65%. Hence, this paper highlights the importance of the proposed method in gaining new insights into OSA’s HRV dynamics, offering a contribution that adds to the existing analytical approaches in the state of the art. |
| format | Article |
| id | doaj-art-f6fbb3e4aff8425b822a7f3e34b2402c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-f6fbb3e4aff8425b822a7f3e34b2402c2025-01-10T13:15:32ZengMDPI AGApplied Sciences2076-34172025-01-0115143310.3390/app15010433Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep ApneaDaniele Padovano0Arturo Martinez-Rodrigo1José M. Pastor2José J. Rieta3Raul Alcaraz4Research Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla-La Mancha, 16002 Cuenca, SpainResearch Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla-La Mancha, 16002 Cuenca, SpainResearch Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla-La Mancha, 16002 Cuenca, SpainBioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, SpainResearch Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla-La Mancha, 16002 Cuenca, SpainObstructive sleep apnea (OSA) represents a significant health concern. While polysomnography (PSG) remains the gold standard, its resource-intensive nature has encouraged the exploration of further alternative approaches. Most of these were based on the heart rate variability (HRV) analysis, but only a few of them have presented a recurrence-based approach. The present paper addresses this gap by integrating convolutional neural networks (CNNs) with HRV recurrence analysis. Employing three different and publicly available databases from PhysioNet’s official repository (Apnea-ECG, MIT-BIH, and UCD-DB), the presented method was able to expose concealed patterns within the distance matrix of HRV’s phase space, which is discernible at an appropriate level of abstraction through CNNs. Under the challenging context of external validation (MIT-BIH and UCD for training, and Apnea-ECG for testing), the results obtained were comparable to those presented in the state of the art, achieving a peak accuracy of 75%, while maintaining balanced sensitivity and specificity at 74% and 75%, respectively. Moreover, these results obtained by the proposed CNN-based recurrence analysis of HRV also outperformed traditional time–frequency models, which have yielded values of accuracy lower than 65%. Hence, this paper highlights the importance of the proposed method in gaining new insights into OSA’s HRV dynamics, offering a contribution that adds to the existing analytical approaches in the state of the art.https://www.mdpi.com/2076-3417/15/1/433sleep apneaheart rate variabilityrecurrencemachine learningdeep learning |
| spellingShingle | Daniele Padovano Arturo Martinez-Rodrigo José M. Pastor José J. Rieta Raul Alcaraz Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea Applied Sciences sleep apnea heart rate variability recurrence machine learning deep learning |
| title | Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea |
| title_full | Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea |
| title_fullStr | Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea |
| title_full_unstemmed | Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea |
| title_short | Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea |
| title_sort | deep learning and recurrence information analysis for the automatic detection of obstructive sleep apnea |
| topic | sleep apnea heart rate variability recurrence machine learning deep learning |
| url | https://www.mdpi.com/2076-3417/15/1/433 |
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