Real-Time Blood Pressure Change Classification Using Enhanced PPG Signals and Convolutional Neural Networks
DOI:
https://doi.org/10.6000/1929-6029.2026.15.15Keywords:
Photoplethysmography, Blood Pressure Classification, Enhanced Signals, Wavelet Denoising, Convolutional Neural Network, Deep LearningAbstract
Photoplethysmography (PPG) is widely used as a non-invasive and cost-effective technique for monitoring cardiovascular activity and assessing blood pressure (BP) variations. However, PPG signals are often affected by noise and motion artifacts, which can reduce signal reliability and negatively impact clinical interpretation and machine learning performance. This study proposes a signal enhancement and classification framework to improve the accuracy of BP change classification using PPG signals. The proposed approach enhances signal quality by reducing noise while preserving important physiological waveform characteristics, enabling more reliable feature extraction. The enhanced signals are then utilized as input to a Convolutional Neural Network (CNN) to learn discriminative temporal and morphological features associated with BP variations. Experimental results demonstrate that the proposed framework achieves a training accuracy of 96.66% and a validation accuracy of 95.31%, outperforming conventional preprocessing approaches such as hard and soft thresholding. These findings highlight the potential of integrating adaptive signal enhancement with deep learning techniques to improve the robustness and reliability of non-invasive BP monitoring systems. The proposed framework offers promising applications for real-time and clinically relevant cardiovascular monitoring.
References
Rastegar S, Gholam Hosseini H, Lowe A. Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges. Physical and Engineering Sciences in Medicine 2019; 43(1): 11-28.
Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement 2007; 28(3): 1-39.
Asad A, Sarwar M, Aslam M, Akpokodje E, Jilani SF. Multiscalefusion-net and resrnn-net: Proposed deep learning architectures for accurate and interpretable pregnancy risk prediction. Applied Sciences 2025; 15(11): 6152.
Batool I. Real-time health monitoring using 5g networks: Deep learning-based architecture for remote patient care. Journal of Medical Internet Research 2025; 6: 70906-70906.
Oleiwi ZC, AlShemmary EN, Al-Augby S. Developing hybrid cnn-gru arrhythmia prediction models using fast fourier transform on imbalanced ecg datasets. Mathematical Modelling of Engineering Problems 2024; 11(2): 413-429.
Donoho D, Johnstone IM. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association 1995; 90(432): 1200-1224.
Donoho D. De-noising by soft-thresholding. IEEE Transactions on Information Theory 1995; 41(3): 613-627.
Donoho D, Johnstone IM. Minimax estimation via wavelet shrinkage. The Annals of Statistics 1998; 26(3).
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaria J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. Journal of Big Data 2021; 8(1).
Alqudah A, Moussavi Z. A review of deep learning for biomedical signals: Current applications, advancements, future prospects, interpretation, and challenges. Computers, Materials & Continua 2025; 83(3): 4567-4592
Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep convolutional neural networks in medical image analysis: A review. Information 2025; 16(3): 195.
Lee J, Mukhanov L, Molahosseini AS, Minhas U, Hua Y, Rincon J, Dichev K, Hong C-H, Vandierendonck H. Resource-efficient convolutional networks: A survey on model-, arithmetic-, and implementation-level techniques. ACM Computing Surveys 2023; 55(13s): 1-36.
Chen S, Ji Z, Wu H, Xu Y. A non-invasive continuous blood pressure estimation approach based on machine learning. Sensors 2019; 19(11): 2585.
Hong J, Jin W, Nandi M, Alastruey J. Real-time classification of blood pressure changes using photoplethysmography and deep learning. Biomedical Signal Processing and Control 2026; 112: 108380.
Lee H-C, Park Y, Yoon SB, Yang SM, Park D, Jung C-W. Vitaldb, a high-fidelity multi-parameter vital signs database in surgical patients. Scientific Data 2022; 9(1).
Lee H-C, Jung C-W. VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Physio Net 2022.
Mallat S. A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, San Diego 1999.
Muralidharan A, Mahfuz S. Human activity recognition using hybrid cnn-rnn architecture. Procedia Computer Science 2025; 257: 336-343
Nurtiyasari D, Rosadi D, Abdurakhman A, Sumardi S. Performance evaluation for Indonesia sharia stocks price prediction integrating wavelet and hybrid approaches. Industrial Engineering & Management Systems 2025; 24(2): 211-224.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
