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Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram
| Content Provider | Scilit |
|---|---|
| Author | Li, Qiao Li, Qichen Liu, Chengyu Shashikumar, Supreeth Prajwal Nemati, Shamim Clifford, Gari D. |
| Copyright Year | 2018 |
| Description | Journal: Physiological Measurement Objective: This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN). Approach: An ECG-derived respiration (EDR) signal and synchronous beat-to-beat heart rate variability (HRV) time series were derived from the ECG using previously described robust algorithms. A measure of cardiorespiratory coupling (CRC) was extracted by calculating the coherence and cross-spectrogram of the EDR and HRV signal in five-minute windows. A CNN was then trained to classify the sleep stages (wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light sleep and NREM deep sleep) from the corresponding CRC spectrograms. A support vector machine was then used to combine the output of CNN with the other features derived from the ECG, including phase-rectified signal averaging (PRSA), sample entropy, as well as standard spectral and temporal HRV measures. The MIT-BIH Polysomnographic Database (SLPDB), the PhysioNet/Computing in Cardiology Challenge 2018 database (CinC2018) and the Sleep Heart Health Study (SHHS) database, all expert-annotated for sleep stages, were used to train and validate the algorithm. Main results: Ten-fold cross validation results showed that the proposed algorithm achieved an accuracy (Acc) of 75.4% and a Cohen's kappa coefficient of K = 0.54 on the out of sample validation data in the classification of Wake, REM, NREM light and deep sleep in SLPDB. This rose to Acc = 81.6% and K = 0.63 for the classification of Wake, REM sleep and NREM sleep and Acc = 85.1% and K = 0.68 for the classification of NREM sleep versus REM/wakefulness in SLPDB. Significance: The proposed ECG-based sleep stage classification approach that represents the highest reported results on non-electroencephalographic data and uses datasets over 10 times larger than those in previous studies. By using a state-of-the-art QRS detector and deep learning model, the system does not require human annotation and can therefore be scaled for mass analysis. |
| Related Links | http://iopscience.iop.org/article/10.1088/1361-6579/aaf339/pdf |
| Ending Page | 124005 |
| Page Count | 1 |
| Starting Page | 124005 |
| ISSN | 09673334 |
| e-ISSN | 13616579 |
| DOI | 10.1088/1361-6579/aaf339 |
| Journal | Physiological Measurement |
| Issue Number | 12 |
| Volume Number | 39 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-11-23 |
| Access Restriction | Open |
| Subject Keyword | Journal: Physiological Measurement Medical Informatics Cardiorespiratory Coupling Cross-time-frequency Domain Deep Convolutional Neural Network Electrocardiogram Sleep Stage Classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physiology Physiology (medical) Biophysics Biomedical Engineering |
