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Detection of sleep apnea syndrome by CNN based on ECG
| Content Provider | Scilit |
|---|---|
| Author | Bai, Yunxiang Zhang, Luqiao Wan, Dechao Xie, Yu Deng, Hanghang |
| Copyright Year | 2021 |
| Description | Journal: Journal of Physics: Conference Series Sleep apnea syndrome is a sleep disease that may lead to sudden death. Long term apnea syndrome can cause chronic cerebral hypoxia, hypertension, cardiovascular and cerebrovascular complications. At present, PSG is the most reliable method for diagnosis. But the diagnosis of PSG is complex and expensive. Electrocardiograph(ECG) and portable medical equipment have been widely used nowadays, which makes the acquisition of ECG signal more and more popular and convenient. In this paper, a convolution neural network based on ECG signal is proposed to predict apnea syndrome, the accuracy and sensitivity of this CNN model for apnea syndrome classification are 94% and 88% respectively. The results show that this method has the advantages of low cost and low complexity. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1757/1/012043/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1757/1/012043 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1757 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Hardware and Architecture Based On Ecg Sleep Apnea Syndrome |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
