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International Journal of Zoology and Applied Biosciences Research Article
Advances in EEG-based sleep apnea detection and treatment monitoring: A survey
Nivedha S, Nirmala B, Lavanya R, Swetha M and Geetha C
Year : 2025 | Pages: 543-546
Received on: 28/09/2025
Revised on: 27/10/2025
Accepted on: 26/11/2025
Published on: 01/12/2025
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Nivedha S, Nirmala B, Lavanya R, Swetha M and Geetha C( 2025).
Advances in EEG-based sleep apnea detection and treatment monitoring: A survey
. International Journal of Zoology and Applied Biosciences, 10( 6), 543-546.
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Abstract
Sleep apnea is a major sleep-related breathing disorder characterized by recurrent episodes of airway obstruction, leading to oxygen desaturation, sleep fragmentation, and long-term cardiovascular and neurological complications. Electroencephalogram (EEG)-based monitoring has emerged as a powerful tool for diagnosing sleep apnea and evaluating treatment efficacy due to its ability to capture brain activity changes associated with arousals, sleep transitions, and respiratory disturbances. This survey provides a comprehensive overview of recent advances in EEG-based sleep apnea detection using machine learning, deep learning, signal processing, and multimodal fusion. The study explores feature extraction methods, apnea event classification, automated scoring algorithms, and EEG-derived biomarkers for therapy monitoring, including CPAP adherence assessment and real-time severity tracking. Trends indicate significant improvements in detection accuracy, reliability, and clinical interpretability with convolutional neural networks (CNNs), recurrent models (RNNs/LSTMs), and transformers. The review highlights challenges such as inter-patient variability, artifacts, data scarcity, and real-time implementation. Future directions include wearable EEG systems, cloud-integrated home monitoring, multimodal fusion with airflow/oxygen signals, and personalized AI models. This survey aims to support researchers and clinicians in understanding current advancements and identifying promising directions for next-generation sleep apnea diagnostic solutions.
Keywords
EEG Monitoring, Polysomnography, Deep Learning, Apnea Detection, CPAP Therapy.
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© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
