A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals
Sleep apnea (SA) is a common chronic sleep breathing disorder, which would cause stroke, cognitive decline, cardiovascular disease, or even death. The SA symptoms often manifest as frequent breathing interruptions during sleep and most individuals with sleeping disorders are not aware of the SA even...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.972581/full |
| _version_ | 1811220391596654592 |
|---|---|
| author | Junyang Chen Mengqi Shen Wenjun Ma Weiping Zheng |
| author_facet | Junyang Chen Mengqi Shen Wenjun Ma Weiping Zheng |
| author_sort | Junyang Chen |
| collection | DOAJ |
| description | Sleep apnea (SA) is a common chronic sleep breathing disorder, which would cause stroke, cognitive decline, cardiovascular disease, or even death. The SA symptoms often manifest as frequent breathing interruptions during sleep and most individuals with sleeping disorders are not aware of the SA events. Using a portable device with single-lead ECG signal is an effective way to help an individual to monitor their sleep conditions at home. However, the SA detection performance of ECG-based methods is still difficult to meet the clinical practice requirement. In this study, we propose an end-to-end spatio-temporal learning-based SA detection method, which consists of multiple spatio-temporal blocks. Each block has the identical architecture with a convolutional neural network (CNN) layer, a max-pooling layer, and a bi-gated recurrent unit (BiGRU) layer. This architecture with repeated spatio-temporal blocks can well capture the morphological spatial feature information as well as the temporal feature information from ECG signals. The proposed SA detection model was evaluated on the publicly available datasets of PhysioNet Apnea-ECG dataset (Apnea-ECG) and University College Dublin Sleep Apnea Database (UCDDB). Extensive experimental results show that our proposed SA model on both Apnea-ECG and UCDDB datasets achieves state-of-the-art results, which are obviously superior to existing ECG-based SA detection methods. It means that our proposed method has the potential to be deployed into a healthcare system to provide a sleep monitoring service, which can screen out SA population with high risk and help to take timely interventions to prevent serious consequences. |
| first_indexed | 2024-04-12T07:42:04Z |
| format | Article |
| id | doaj.art-4e17390c0e194ec29053db2816b6cda7 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-12T07:42:04Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-4e17390c0e194ec29053db2816b6cda72022-12-22T03:41:48ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-08-011610.3389/fnins.2022.972581972581A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signalsJunyang Chen0Mengqi Shen1Wenjun Ma2Weiping Zheng3School of Computer Science, South China Normal University, Guangzhou, ChinaGrado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United StatesSchool of Computer Science, South China Normal University, Guangzhou, ChinaSchool of Computer Science, South China Normal University, Guangzhou, ChinaSleep apnea (SA) is a common chronic sleep breathing disorder, which would cause stroke, cognitive decline, cardiovascular disease, or even death. The SA symptoms often manifest as frequent breathing interruptions during sleep and most individuals with sleeping disorders are not aware of the SA events. Using a portable device with single-lead ECG signal is an effective way to help an individual to monitor their sleep conditions at home. However, the SA detection performance of ECG-based methods is still difficult to meet the clinical practice requirement. In this study, we propose an end-to-end spatio-temporal learning-based SA detection method, which consists of multiple spatio-temporal blocks. Each block has the identical architecture with a convolutional neural network (CNN) layer, a max-pooling layer, and a bi-gated recurrent unit (BiGRU) layer. This architecture with repeated spatio-temporal blocks can well capture the morphological spatial feature information as well as the temporal feature information from ECG signals. The proposed SA detection model was evaluated on the publicly available datasets of PhysioNet Apnea-ECG dataset (Apnea-ECG) and University College Dublin Sleep Apnea Database (UCDDB). Extensive experimental results show that our proposed SA model on both Apnea-ECG and UCDDB datasets achieves state-of-the-art results, which are obviously superior to existing ECG-based SA detection methods. It means that our proposed method has the potential to be deployed into a healthcare system to provide a sleep monitoring service, which can screen out SA population with high risk and help to take timely interventions to prevent serious consequences.https://www.frontiersin.org/articles/10.3389/fnins.2022.972581/fullsleep apneaECG signalsspatio-temporal learningBiGRUattention |
| spellingShingle | Junyang Chen Mengqi Shen Wenjun Ma Weiping Zheng A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals Frontiers in Neuroscience sleep apnea ECG signals spatio-temporal learning BiGRU attention |
| title | A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals |
| title_full | A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals |
| title_fullStr | A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals |
| title_full_unstemmed | A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals |
| title_short | A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals |
| title_sort | spatio temporal learning based model for sleep apnea detection using single lead ecg signals |
| topic | sleep apnea ECG signals spatio-temporal learning BiGRU attention |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.972581/full |
| work_keys_str_mv | AT junyangchen aspatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT mengqishen aspatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT wenjunma aspatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT weipingzheng aspatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT junyangchen spatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT mengqishen spatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT wenjunma spatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals AT weipingzheng spatiotemporallearningbasedmodelforsleepapneadetectionusingsingleleadecgsignals |