MAF-SleepNet: A Multimodal Attention-Enhanced Fusion Network for Automatic Multi-Class Sleep Disorder Classification from Polysomnography.

Overview

1. Department of Biomedical, Vocational School of Technical Sciences, Firat University, 23119 Elazig, Türkiye
2. School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
3. Department of Electrical-Electronics Engineering, Faculty of Engineering, Firat University, 23119 Elazig, Türkiye
4. School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia

Abstract

Background/Objectives: Sleep disorders are heterogeneous conditions with diverse neural, muscular, and ocular manifestations, making polysomnography (PSG) the gold standard for accurate diagnosis. Artificial intelligence-based approaches, particularly deep learning (DL) models capable of integrating heterogeneous information, offer a promising solution for reliable decision-making in such clinical scenarios. However, most existing DL studies have focused on a single disorder, relied on limited datasets, or employed epoch-level labeling strategies that overlook the episodic nature of sleep pathophysiology, thereby limiting clinical applicability. To address these gaps, we propose a novel multimodal attention-enhanced fusion network (MAF-SleepNet) for automatic multi-class sleep disorder classification based on the International Classification of Sleep Disorders. Methods: MAF-SleepNet jointly processes electroencephalography (EEG), electrooculography (EOG), and leg electromyography (EMG) signals through modality-specific feature extraction and adaptive attention mechanisms, capturing both intra- and inter-modality dependencies. The model was evaluated on a combined dataset of 141 recordings from three public databases, including five PSG-requiring disorders and a healthy class. Results: Experimental results demonstrated that MAF-SleepNet achieved 86.07 ± 3.66% accuracy and 82.67 ± 4.46% macro-F1 under a strict subject-independent cross-validation, and 98.96 ± 0.66% accuracy and 98.93 ± 0.60% macro-F1 under subject-dependent cross-validation. Conclusions: These results demonstrate that the proposed approach provides a more reliable and clinically meaningful assessment compared to many existing studies that rely on subject-dependent evaluation or epoch-level labeling. The findings highlight the effectiveness of adaptive multimodal fusion for robust and clinically relevant sleep disorder classification. Future work should investigate the integration of respiratory and autonomic modalities and validation on larger multi-center cohorts.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• doi:10.13026/p2rp-sg37, at the source; found in the references

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 2 funders, 69 references.

Cite

This paper

Yaman, S., Guler, H., & Hafeez-Baig, A. (2026). MAF-SleepNet: A Multimodal Attention-Enhanced Fusion Network for Automatic Multi-Class Sleep Disorder Classification from Polysomnography. Diagnostics (Basel, Switzerland), 16(15), 2317. https://doi.org/10.3390/diagnostics16152317

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/s26134091

Hypnogram-Driven Automatic Sleep Staging and a Quality-Index Assessment Through a Two-Stage LSTM-DNN Ensemble Learning Approach Using Multi-Biosignal Features for Sleep Disorder Detection.

Journal: Sensors (Basel, Switzerland)

In common: sleep disorders, EEG, 2 references

[2] doi:10.1093/sleepadvances/zpag051 [code]

What matters beyond model choice for wearable sleep staging? How personalization, evaluation choices, and easy-to-classify wake impact performance.

Journal: Sleep advances : a journal of the Sleep Research Society

In common: sleep disorders, EEG, 1 reference

[3] doi:10.3390/diagnostics16132012

Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.

Journal: Diagnostics (Basel, Switzerland)

In common: EEG, 2 references

[4] doi:10.7717/peerj.21426 [code]

Integrated transcriptomic identification and validation reveal key autophagy-associated biomarkers in sleep deprivation.

Journal: PeerJ

In common: sleep disorders, 1 reference

[5] doi:10.1038/s41531-026-01360-5 [code]

Hippocampal atrophy in untreated de novo Parkinson's disease with obstructive sleep apnea.

Journal: NPJ Parkinson's disease

In common: sleep disorders, 1 reference

[6] doi:10.1098/rstb.2024.0461 [code]

Shallow recurrent decoders for neural and behavioural dynamics.

Journal: Philosophical transactions of the Royal Society of London. Series B, Biological sciences

In common: 2 references

[7] doi:10.1016/j.nbscr.2026.100147

Analytical amplitude/phase markers for neurological sleep apnea events.

Journal: Neurobiology of sleep and circadian rhythms

In common: sleep disorders, 1 reference

[8] doi:10.1371/journal.pone.0352640 [code]

Automated sleep scoring in hibernating and non-hibernating American black bears.

Journal: PloS one

In common: EEG, 1 reference

[9] doi:10.1093/sleepadvances/zpag069 [code]

Thalamic transcranial electrical stimulation with temporal interference enhances sleep spindle activity during a daytime nap.

Journal: Sleep advances : a journal of the Sleep Research Society

In common: EEG, 1 reference

[10] doi:10.1038/s41598-026-57532-z

Predictability of sleep slow oscillation emergence and spatial extent from pre-onset neural dynamics.

Journal: Scientific reports

In common: EEG, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.