During sleep our brain goes through a series of changes between different sleep stages, which are characterized by specific brain and body activity patterns. The sleep stages can be determined by measuring the neuronal activity in the cerebral cortex (via electroencephalography, EEG), eye movements (via electrooculography, EOG), and/or the activity of facial muscles (via electromyography, EMG) in a polysomnography (PSG) study. U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data. It automates the classification into sleep stages. It is inspired by the popular U-Net architecture originally proposed for image segmentation and temporal convolutional networks. U-Time adopts basic concepts from U-Net for 1D time-series segmentation by mapping a whole sequence to a dense segmentation in a single forward pass.
Input variables : Physiological signal (EEG Data)
Output Variables : Sleep stages
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | November, 2019 |
| Healthcare Domain | : | Payer |
| Code | : | github.com |
| Member Experience |
| Behavioral Health |