Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal
| العنوان: | Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal |
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| المؤلفون: | Erdenebayar Urtnasan, Kyoung-Joung Lee, Jong-Uk Park |
| المصدر: | Neural Computing and Applications. 32:4733-4742 |
| بيانات النشر: | Springer Science and Business Media LLC, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 0209 industrial biotechnology, Computer science, business.industry, Pattern recognition, 02 engineering and technology, Signal, 020901 industrial engineering & automation, Recurrent neural network, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Breathing, Sleep disordered breathing, Computational Science and Engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software |
| الوصف: | In this study, we propose a novel method for automatically detecting sleep-disordered breathing (SDB) events using a recurrent neural network (RNN) to analyze nocturnal electrocardiogram (ECG) recordings. We design a deep RNN model comprising six stacked recurrent layers for the automatic detection of SDB events. The proposed deep RNN model utilizes long short-term memory (LSTM) and a gated-recurrent unit (GRU). To evaluate the performance of the proposed RNN method, 92 SDB patients were enrolled. Single-lead ECG recordings were measured for an average 7.2-h duration and segmented into 10-s events. The dataset comprised a training dataset (68,545 events) from 74 patients and test dataset (17,157 events) from 18 patients. The proposed method achieved high performance with an F1-score of 98.0% for LSTM and 99.0% for GRU. The results demonstrate superior performance over conventional methods. The proposed method can be used as a precise screening and diagnosing tool for patients with SDB disorders. |
| تدمد: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-018-3833-2 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::d7bf551df8235c7bd0887a69fdb7181d https://doi.org/10.1007/s00521-018-3833-2 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........d7bf551df8235c7bd0887a69fdb7181d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14333058 09410643 |
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| DOI: | 10.1007/s00521-018-3833-2 |