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Peer-reviewedCC BYFrontiers in physiology

An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders

  • Shaween Shukir1
  • Mostafa Moussa1
  • Yahya AlZaabi1
  • Ahsan Khandoker1
  • Zbigniew R. Struzik2
  1. 1Healthcare Engineering Innovation Group (HEIG), Department of Biomedical Engineering and Biotechnology, Khalifa University , ,
  2. 2Graduate School of Education, University of Tokyo , , ,
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Abstract

Background: Multimodal sleep analysis typically requires separate processing pipelines for electroencephalography (EEG) and electrocardiography (ECG), which complicates joint investigation of sleep microstructure and autonomic regulation. Cyclic alternating pattern (CAP) scoring and heart rate variability (HRV) analysis are routinely performed in different software environments, limiting temporal alignment, reproducibility, and scalability.

Methods: We present an open-source MATLAB framework that integrates rule-based CAP A1 detection and HRV feature extraction within a single, time-synchronized workflow. The pipeline performs standardized EEG and ECG preprocessing, rule-based CAP A1 detection during slow-wave sleep (SWS) implementing the Terzano scoring criteria, R-peak detection via the Pan–Tompkins algorithm, and computation of time-domain, frequency-domain, and nonlinear HRV features by calling the open-source PhysioZoo/mhrv toolbox directly. CAP annotations are aligned with 5-minute ECG epochs through the shared polysomnography (PSG) time base. The complete code is released as an open-source repository to support reuse and extension.

Results: Because HRV features are computed by calling the PhysioZoo/mhrv toolbox directly, the pipeline introduces no reimplemented HRV routine and inherits that toolbox’s validated feature definitions. CAP A1 detection is provided as a rule-based implementation of published criteria and has not yet been benchmarked against expert manual scoring. A worked example on the OSAD+ subgroup (n = 18) demonstrates pipeline output across CAP and non-CAP epochs, and a healthy-sleep A1-event HRV reference is reported from the public CAP Sleep Database (15 controls, 785 A1 epochs) as a descriptive cross-database reference.

Conclusion: The framework provides a reproducible, rule-based, and modular tool for integrated EEG–ECG analysis of sleep microstructure and autonomic dynamics. By enabling synchronized CAP–HRV feature extraction in a single environment, it lowers the engineering barrier to brain–heart coupling research during sleep and provides a foundation for future extensions, including validated CAP detection, a matched-control study, a Python port, and cloud-based deployment.

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Cite

BibTeXRIS
BibTeX
@article{shukir2026automated,
  title = {{An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders}},
  author = {Shaween Shukir and Mostafa Moussa and Yahya AlZaabi and Ahsan Khandoker and Zbigniew R. Struzik},
  journal = {Frontiers in physiology},
  year = {2026},
  month = oct,
  doi = {10.3389/fphys.2026.1864664},
  url = {https://europepmc.org/article/PMC/PMC13719611},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders
AU  - Shaween Shukir
AU  - Mostafa Moussa
AU  - Yahya AlZaabi
AU  - Ahsan Khandoker
AU  - Zbigniew R. Struzik
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in physiology
DO  - 10.3389/fphys.2026.1864664
UR  - https://europepmc.org/article/PMC/PMC13719611
AB  - Background: Multimodal sleep analysis typically requires separate processing pipelines for electroencephalography (EEG) and electrocardiography (ECG), which complicates joint investigation of sleep microstructure and autonomic regulation. Cyclic alternating pattern (CAP) scoring and heart rate variability (HRV) analysis are routinely performed in different software environments, limiting temporal alignment, reproducibility, and scalability.  Methods: We present an open-source MATLAB framework that integrates rule-based CAP A1 detection and HRV feature extraction within a single, time-synchronized workflow. The pipeline performs standardized EEG and ECG preprocessing, rule-based CAP A1 detection during slow-wave sleep (SWS) implementing the Terzano scoring criteria, R-peak detection via the Pan–Tompkins algorithm, and computation of time-domain, frequency-domain, and nonlinear HRV features by calling the open-source PhysioZoo/mhrv toolbox directly. CAP annotations are aligned with 5-minute ECG epochs through the shared polysomnography (PSG) time base. The complete code is released as an open-source repository to support reuse and extension.  Results: Because HRV features are computed by calling the PhysioZoo/mhrv toolbox directly, the pipeline introduces no reimplemented HRV routine and inherits that toolbox’s validated feature definitions. CAP A1 detection is provided as a rule-based implementation of published criteria and has not yet been benchmarked against expert manual scoring. A worked example on the OSAD+ subgroup (n = 18) demonstrates pipeline output across CAP and non-CAP epochs, and a healthy-sleep A1-event HRV reference is reported from the public CAP Sleep Database (15 controls, 785 A1 epochs) as a descriptive cross-database reference.  Conclusion: The framework provides a reproducible, rule-based, and modular tool for integrated EEG–ECG analysis of sleep microstructure and autonomic dynamics. By enabling synchronized CAP–HRV feature extraction in a single environment, it lowers the engineering barrier to brain–heart coupling research during sleep and provides a foundation for future extensions, including validated CAP detection, a matched-control study, a Python port, and cloud-based deployment.
SN  - 1664-042X
ER  -