| Autor: |
Seola Kim, Hyun-Soo Choi, Dohyun Kim, Minkyu Kim, Seo-Young Lee, Jung-Kyeom Kim, Yoon Kim, Woo Hyun Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Diagnostics, Vol 14, Iss 11, p 1134 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2075-4418 |
| DOI: |
10.3390/diagnostics14111134 |
| Popis: |
This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea–hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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