Accurate detection of sleep apnea with long short-term memory network based on RR interval signals
Article
Faust, Oliver, Barika, Ragab Ragab, Shenfield, Alex, Ciaccio, Edward J. and Acharya, U. Rajendra. 2021. "Accurate detection of sleep apnea with long short-term memory network based on RR interval signals." Knowledge-Based Systems. 212. https://doi.org/10.1016/j.knosys.2020.106591
| Article Title | Accurate detection of sleep apnea with long short-term memory network based on RR interval signals |
|---|---|
| ERA Journal ID | 18062 |
| Article Category | Article |
| Authors | Faust, Oliver, Barika, Ragab Ragab, Shenfield, Alex, Ciaccio, Edward J. and Acharya, U. Rajendra |
| Journal Title | Knowledge-Based Systems |
| Journal Citation | 212 |
| Number of Pages | 10 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0950-7051 |
| 1872-7409 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2020.106591 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0950705120307206 |
| Abstract | Sleep apnea is a common condition that is characterized by sleep-disordered breathing. Worldwide the number of apnea cases has increased and there has been a growing number of patients suffering from apnea complications. Unfortunately, many cases remain undetected, because expensive and inconvenient examination methods are formidable barriers with regard to the diagnostics. Furthermore, treatment monitoring depends on the same methods which also underpin the initial diagnosis; hence issues related to the examination methods cause difficulties with managing sleep apnea as well. Computer-Aided Diagnosis (CAD) systems could be a tool to increase the efficiency and efficacy of diagnosis. To investigate this hypothesis, we designed a deep learning model that classifies beat-to-beat interval traces, medically known as RR intervals, into apnea versus non-apnea. The RR intervals were extracted from Electrocardiogram (ECG) signals contained in the Apnea-ECG benchmark Database. Before feeding the RR intervals to the classification algorithm, the signal was band-pass filtered with an Ornstein–Uhlenbeck third-order Gaussian process. 10-fold cross-validation indicated that the Long Short-Term Memory (LSTM) network has 99.80% accuracy, 99.85% sensitivity, and 99.73% specificity. With hold-out validation, the same network achieved 81.30% accuracy, 59.90% sensitivity, and 91.75% specificity. During the design, we learned that the band-pass filter improved classification accuracy by over 20%. The increased performance resulted from the fact that neural activation functions can process a DC free signal more efficiently. The result is likely transferable to the design of other RR interval based CAD systems, where the filter can help to improve classification performance. |
| Keywords | Deep learning; Sleep apnea; Heart rate variability; Detrending |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Byline Affiliations | Sheffield Hallam University, United Kingdom |
| Columbia University, United States | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| School of Management and Enterprise |
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