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Early Screening of Sleep-Disordered Breathing Using Metaheuristic-Optimized Extreme Learning Machines.

Thaer Thaher¹, Alaa Sheta^2,3, Huthaifa I Ashqar^4,5

¹Department of Computer Systems Engineering, Arab American University, Jenin P.O. Box 240, Palestine.

Diagnostics (Basel, Switzerland)

|July 15, 2026

Summary

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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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This study developed a lightweight Extreme Learning Machine (ELM) framework optimized with metaheuristics for diagnosing obstructive sleep apnea (OSA) and sleep-disordered breathing (SDB). Metaheuristic optimization showed moderate improvements in ELM performance for OSA screening.

Area of Science:

Computational intelligence and machine learning applied to medical diagnostics.
Development of optimized algorithms for sleep disorder classification.

Background:

Obstructive sleep apnea (OSA) is a prevalent sleep disorder requiring efficient diagnostic tools.
Traditional methods like polysomnography are accurate but impractical for large-scale screening.
Need for cost-effective and time-efficient diagnostic frameworks for OSA and related sleep-disordered breathing (SDB).

Purpose of the Study:

To propose and evaluate a lightweight diagnostic framework using Extreme Learning Machine (ELM) optimized by metaheuristic algorithms.
To assess the impact of metaheuristic optimization on ELM classification performance for OSA and SDB.
To investigate the utility of demographic, clinical, and sleep-related predictors in an optimized ELM model.

Main Methods:

Keywords:

extreme learning machine machine learning metaheuristic optimization obstructive sleep apnea screening sleep-disordered breathing

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Developed an Extreme Learning Machine (ELM) model optimized with basic and advanced metaheuristic algorithms.
Trained and validated the framework on two datasets: a clinical OSA dataset (274 subjects) and a public SDB dataset (500 subjects).
Compared the optimized ELM against eight baseline classifiers and analyzed feature importance using permutation importance and SHAP.

Main Results:

Metaheuristic optimization moderately improved ELM performance on the OSA dataset, increasing ROC-AUC from 0.6527 to 0.73.
On the imbalanced SDB dataset, modest ROC-AUC gains were observed (0.5132 to ~0.54), with minor decreases in accuracy and F1-score.
Feature importance analysis indicated reliance on diagnosis-derived predictors for classification.

Conclusions:

The proposed metaheuristic-optimized ELM framework offers a lightweight, low-inference-cost decision-support tool for OSA/SDB screening.
The approach shows potential for screening applications, but requires further validation with larger cohorts and screening-specific features.
Clinical implementation necessitates further validation before widespread adoption.