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Sleep Apnea

Sleep Apnea

Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...

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Development of a Machine Learning Model for Screening Sleep Apnea in Heart Failure Patients Using Sleep Sensor Data.

Mathushan Gunasegarama¹, Birthe Dinesen¹, Nikolaj Müller Larsen¹

¹Laboratory Welfare Technologies - Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark.

Studies in Health Technology and Informatics

|October 3, 2025

Summary

This study explored using machine learning to screen for sleep apnea (SA) in heart failure (HF) patients. The developed model shows promise, but requires more data for better accuracy in SA screening.

Keywords:

Heart Failure Machine Learning Screening tool Sleep Apnea Sleep Sensor Telemonitoring

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Area of Science:

Cardiology
Pulmonology
Medical Informatics

Background:

Sleep apnea (SA) is common in heart failure (HF) patients, increasing complication risks.
Early SA detection in HF is crucial for effective treatment and improved patient outcomes.
Current screening methods may not be fully optimized for the HF population.

Purpose of the Study:

To assess the feasibility of a machine learning (ML) based screening tool for SA in HF patients.
To develop and evaluate a predictive model using data from the Future Patient Telerehabilitation program.
To identify potential improvements for future SA screening tool development in HF.

Main Methods:

Utilized a random forest classifier to build a predictive model for SA screening.
Employed data from the Future Patient Telerehabilitation program for model development.
Evaluated model performance using the receiver operating characteristic area under the curve (ROC-AUC).

Main Results:

The random forest model achieved a promising ROC-AUC of 0.85.
The ML model demonstrated potential as a SA screening tool for HF patients.
Model generalizability was limited by the absence of key predictors like oxygen saturation.

Conclusions:

Machine learning models show potential for screening sleep apnea in heart failure patients.
Further research with comprehensive, standardized data is needed to enhance model accuracy and clinical utility.
High-quality data from future clinical trials is essential for advancing SA screening in HF.