Select a model.

Respiratory Events

Scoring

Select a type of scoring.

Trained Model

Select a previously trained model.

Train a new predictive model for sleep staging, arousal, or respiratory scoring of polysomnographic recording. While training is in progress, you may navigate away from the page or close your browser. Trained models may be saved for later use. Select an existing model to test on out-of-sample data, or configure and train a new model.

Model configuration.

Model Type

Configuration

Multiclass models use a softmax function to produce probabilities for each sleep stage or respiratory event for each analysed epoch of recording. The maximum allowed tree depth (or path length) limits the complexity of each decision tree. Higher values allow trees to model higher-order feature interactions, and increase model capacity, complexity, and the likelihood of overfitting. Lower values constrain complexity and may improve generalisation. Maximum number of histogram bins per feature used to approximate candidate split thresholds during tree building. Higher values allow finer splits, while lower values coarsen the candidate thresholds.

None Weight

Ob Apn Weight

Ce Apn Weight

Hypopn Weight

Class imbalance (highly uneven class representation within the training data) may be corrected by adjusting class weights.

Model Information

Alpha

Lambda

Gamma

Base Scores

Features

Trees

Feature Selection

	Feature	Description
	Phase Mean Difference	Difference between the Phase Mean of the current window and a 40-second baseline.
	Phase Mean	Mean of the Phase.
	Phase Positive Fraction	Fraction of the Phase above a positive deadband.
	Phase Negative Fraction	Fraction of the Phase below a negative deadband.
	Phase Positive Area	Area of the Phase above a positive deadband.
	Phase Negative Area	Area of the Phase below a negative deadband.
	Phase Spread Difference	Difference between the Phase Spread of the current window and a 40-second baseline.
	Phase RMS Difference	Difference between the Phase RMS of the current window and a 40-second baseline.
	Phase Spread	Spread (P90 - P10) of the Phase.
	Phase RMS	Root mean square of the Phase.
	Phase Permutation Entropy	Permutation entropy of the Phase where the embedding delay equals 1, and m equals 3. A dynamic measure of regularity.
	Sum Inspiratory Area Difference	Difference between the Sum Inspiratory Area of the current window and a 40-second baseline.
	Sum Inspiratory Area	Inspiratory Area of the Sum.
	Sum SSFD	Sum of squared first differences in the Sum.
	Sum Zero Crossings	Number of sign changes between consecutive samples of the Sum.
	Sum Spread Difference	Difference between the Sum Spread of the current window and a 40-second baseline.
	Sum RMS Difference	Difference between the Sum RMS of the current window and a 40-second baseline.
	Sum Spread	Spread (P90 - P10) of the Sum.
	Sum RMS	Root mean square of the Sum.
	Sum Permutation Entropy	Permutation entropy of the Sum where the embedding delay equals 1, and m equals 3. A dynamic measure of regularity.
	Flow Inspiratory Peak Difference	Difference between the Inspiratory P95 of the Flow in the current window and a 40-second baseline.
	Flow Inspiratory Area Difference	Difference between the Inspiratory Area of the Flow in the current window and a 40-second baseline.
	Flow Inspiratory Peak	Inspiratory P95 of the Flow.
	Flow Inspiratory Area	Inspiratory Area of the Flow.
	Flow Inspiratory Ratio	Ratio of the mean Inspiratory Flow to the Inspiratory P95.
	Flow Inspiratory Fraction	Fraction of Inspiratory Flow above 80% of the Inspiratory P95.
	Flow SSFD	Sum of squared first differences in the Flow.
	Flow Zero Crossings	Number of sign changes between consecutive samples of the Flow.
	Flow Spread Difference	Difference between the Flow Spread (P90 - P10) of the current window and a 40-second baseline.
	Flow RMS Difference	Difference between the Flow RMS of the current window and a 40-second baseline.
	Flow Spread	Spread (P90 - P10) of the Flow.
	Flow RMS	Root mean square of the Flow.
	Flow Permutation Entropy	Permutation entropy of the Flow where the embedding delay equals 1, and m equals 3. A dynamic measure of regularity.
	Oronasal Inspiratory Peak Difference	Difference between the Inspiratory P95 of the Oronasal Flow in the current window and a 40-second baseline.
	Oronasal Inspiratory Area Difference	Difference between the Inspiratory Area of the Oronasal Flow in the current window and a 40-second baseline.
	Oronasal Inspiratory Peak	Inspiratory P95 of the Oronasal Flow.
	Oronasal Inspiratory Area	Inspiratory Area of the Oronasal Flow.
	Oronasal SSFD	Sum of squared first differences in the Oronasal Flow.
	Oronasal Zero Crossings	Number of sign changes between consecutive samples of the Oronasal Flow.
	Oronasal Spread Difference	Difference between the Oronasal Flow Spread (P90 - P10) of the current window and a 40-second baseline.
	Oronasal RMS Difference	Difference between the Oronasal Flow RMS of the current window and a 40-second baseline.
	Oronasal Spread	Spread (P90 - P10) of the Oronasal Flow.
	Oronasal RMS	Root mean square of the Oronasal Flow.
	Oronasal Permutation Entropy	Permutation entropy of the Oronasal Flow where the embedding delay equals 1, and m equals 3. A dynamic measure of regularity.

Select training data.

Dataset

Select a precomputed dataset to use for model training. Only datasets specifically configured for training are available. Cross-validation results are available during model testing.

Training configuration and summary.

Bayesian Optimization

Optimization Iterations

Number of objective function evaluations.

Eta Minimum

Gamma Minimum

Lambda Minimum

MinChildWeight Minimum

Subsample Minimum

Optimization Candidates

Number of candidates evaluated per iteration.

Eta Maximum

Gamma Maximum

Lambda Maximum

MinChildWeight Maximum

Subsample Maximum

Configure the Bayesian Optimizer and search space for each hyperparameter. The number of iterations determines how many points are evaluated, and the number of candidates controls the resolution of the acquisition search.

Training Progress

Finished

Best Parameters: Eta=0.013, Gamma=3.76, Lambda=0.002, MinChildWeight=37.906, Subsample=0.864 Selected Parameters: Eta=0.013, Gamma=3.76, Lambda=0.002, MinChildWeight=37.906, Subsample=0.864

0

0.4

0.8

Iteration

Multiclass Log Loss

Bayesian Optimization is used to search the hyperparameter ranges provided. This process attempts to balance exploration and exploitation to select the optimal values for the trained model. Gaussian Process Regression (GPR) and the Expected Improvement acquisition function are used. For each set of hyperparameters, models are trained using the appropriate solver. K-fold cross-validation is used to produce a validation error which is input into the GPR that underpins the Bayesian Optimization. The most optimal hyperparameters, as determined by the optimization process, are then used to train the final model. You may save the trained model when training is complete. Saved models are accessible immediately through the Analysis area of the platform.

Model Name

Model validation and testing.

Test Dataset

Select a precomputed dataset to use for out-of-sample Model Testing. Out-of-sample data are recommended.

Model Validation

Confusion Matrix

		Actual
		None	Ob Apn	Ce Apn	Hypopn
Predicted	None	19174	406	301	1868
	Ob Apn	53	167	18	153
	Ce Apn	36	21	132	33
	Hypopn	726	404	151	1357

Performance Metrics

Accuracy: 83%

Precision: 0.604

Recall: 0.436

F1: 0.482

Model Accuracy, Precision, Recall, and F1 are the macro-average of per-class scores using the arithmetic mean.No Ev = No Event, Ob Apn = Obstructive Apnea, Ce Apn = Central Apnea, Hypopn = Hypopnea.

Model Validation data are collected from out-of-fold testing during cross-validation. These data are saved with the model and do not change. Only the first 25,000 samples are displayed.

Model Testing

Confusion Matrix

		Actual
		None	Ob Apn	Ce Apn	Hypopn
Predicted	None	46847	83	60	353
	Ob Apn	160	41	5	24
	Ce Apn	128	3	31	4
	Hypopn	1821	97	31	312

Performance Metrics

Accuracy: 94%

Precision: 0.373

Recall: 0.459

F1: 0.394

Model Accuracy, Precision, Recall, and F1 are the macro-average of per-class scores using the arithmetic mean.No Ev = No Event, Ob Apn = Obstructive Apnea, Ce Apn = Central Apnea, Hypopn = Hypopnea.

Model Testing using the selected dataset. Data are out-of-sample and so testing results provide a reasonable indication of model generalisability. Data are not consecutive epochs from any single recording and so logarithmic smoothing is not applied.Paired label and prediction data are available for download. Classification data are represented as No Event (0), Obstructive Apnea (1), Central Apnea (2), and Hypopnea (3).