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Lightweight ResNet-Based Deep Learning for Photoplethysmography Signal Quality Assessment.

Yangyang Zhao, Matti Kaisti, Olli Lahdenoja

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
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    This study introduces a lightweight deep learning model for assessing photoplethysmography (PPG) signal quality, significantly reducing computational resources for wearable devices and improving cardiovascular monitoring accuracy.

    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Wearable Technology

    Background:

    • Deep learning models are increasingly used in wearable devices, but computational constraints require lightweight and efficient designs.
    • Photoplethysmography (PPG) signal quality assessment (SQA) is vital for reliable cardiovascular monitoring using wearables.
    • Preprocessing methods can enhance the performance of deep learning models for PPG analysis.

    Purpose of the Study:

    • To develop a lightweight deep learning framework for PPG signal quality assessment (SQA).
    • To evaluate the impact of different input configurations (PPG, derivatives, autocorrelation) on SQA performance.
    • To compare the proposed model's efficiency and effectiveness against existing studies.

    Main Methods:

    • A ResNet-based deep learning framework incorporating Squeeze-and-Excitation (SE) modules was proposed.

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  • The model was trained and tested using the Moore4Medical (M4M) and MIMIC-IV datasets.
  • Various input channel combinations, including PPG signal, first derivative (FDP), second derivative (SDP), and autocorrelation (ATC), were explored.

    • Main Results:

    • The model achieved high performance, with up to 96.52% AUC on the M4M dataset and 84.43% AUC on the MIMIC-IV dataset.
    • Significant reductions in model parameters (over 99%) and FLOPs (over 60%) were achieved compared to existing methods.
    • The M4M dataset, novel in its focus on PPG for atrial fibrillation (AF) detection, was utilized.

    Conclusions:

    • The proposed lightweight framework offers efficient and accurate PPG SQA for resource-limited wearable devices.
    • This technology can enhance the reliability of continuous cardiovascular monitoring, supporting clinical decisions in telemedicine and remote care.
    • The model's reduced computational footprint facilitates practical deployment and broader adoption in wearable health technology.