This service uses machine learning (ML) to analyze polysomnography (PSG) data in children. Computed statistical, non-linear, and spectral features are used for sleep staging and respiratory scoring.

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

Sleep Staging

Sleep staging uses 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 stabilize scored stages and transitions. Scoring data are used to compute sleep statistics including sleep duration, latency, efficiency, and WASO.

Respiratory Scoring

Respiratory scoring uses multiclass classification and the softmax function. Trained model outputs are refined using pulse oximetry data if provided. Scoring data are used to compute respiratory statistics including the apnea-hypopnea index (AHI), central AHI, and obstructive AHI.

Online Retraining

This service 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 are Gradient Boosting Decision Trees that use an XGBoost-like algorithm and are trained using a Histogram-based solver. Multinomial Regression models are trained using the Limited-memory BFGS method with strong Wolfe conditions. The performance of these solvers is comparable to other widely used machine learning libraries.