Automated Analysis

This demo uses machine learning (ML) to analyse polysomnography (PSG) data in children. Computed statistical, non-linear, and spectral features are used in the prediction process.

Sleep Staging is performed using multiclass classification and the Softmax function. Thirty-second segments (epochs) of the recording are classified as Stage Wake, N1, N2, N3, or REM. Log probability smoothing is then used to stabilise scored stages and transitions. These data are used to compute sleep statistics traditionally obtained from overnight polysomnography, such as sleep duration, latency, efficiency, and WASO.

Analysis of recording uses a multiclass classifier and the Softmax function. Log probability smoothing is then used to stabilise scored stages and transitions. Raw Softmax probabilities are available in per-epoch analysis.

Peer-reviewed article containing out-of-sample performance data coming soon.

Online Retraining

This demo offers a streamlined pipeline for training and validating models using a bank of pre-computed features and datasets. Newly trained models are immediately available for use after saving, enabling rapid prototyping and rollout of updated models. All models produce a set of calibrated probabilities for each stage of sleep.

Model training uses a set of custom solvers. Gradient Boosting Machines (GBMs) are Gradient Boosting Decision Trees that use an XGBoost-like algorithm and are trained using the Histogram Tree Method. The performance of these solvers is comparable to other widely used machine learning libraries.