Deep Spectra P2E: AI-Driven Spectral ECG Reconstruction from PPG for Continuous Cardiovascular Monitoring
Corresponding author: Shyamala Subramanian
Abstract
The increasing prevalence of cardiovascular diseases demands affordable, accessible, and continuous cardiac monitoring solutions. Although an Electrocardiogram (ECG) is considered the clinical gold standard, long-term real-time monitoring of healthy individuals is limited. On the other hand, photoplethysmography is a simple and cost-effective technique, but it can be susceptible to noise and signal distortion. This study attempts to bridge the gap by proposing a novel framework for Photoplethysmogram (PPG) to ECG transformation, using spectral transformation methods such as Short-Time Fourier Transform (STFT), Discrete Cosine Transform (DCT), Wavelet Transform (WT), and Fast Fourier Transform (FFT). Deep learning models, namely Convolutional Neural Network (CNN), Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), were trained on the UCI Machine Learning Repository version of the MIMIC II dataset and PulseDB Vital datasets to reconstruct ECG from PPG, achieving a correlation coefficient of 0.9731. Validated on MIMIC III, robustness was confirmed with a correlation of 0.9639. The proposed framework leverages frequency-domain representations and lightweight, efficient deep learning models, making it well-suited for integration into real-time and resource-limited environments, such as wearable health monitoring systems, and serves as a foundation for future non-invasive blood pressure estimation using only PPG input.
Keywords:
Photoplethysmogram (PPG), Electrocardiogram (ECG), reconstruction, cardiovascular diseases, CNN-LSTM, CNN-GRU, CNN, BP estimation, mean absolute error, standard deviationDownloads
References
"Cardiovascular Diseases (CVD)," World Health Organization, https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
"World Heart Report 2023: Full Report," World Heart Federation. https://world-heart-federation.org/resource/world-heart-report-2023/.
Y. Sattar and L. Chhabra, ''Electrocardiogram,'' in StatPearls, Treasure Island, FL, USA: StatPearls Publishing, 2025.
M. Tounsi, H. Ali, A. T. Azar, A. Al-Khayyat, and I. K. Ibraheem, ''Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning-based ECG Signal Classification on Internet of Things Environment.'' Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19492–19500, Feb. 2025. DOI: https://doi.org/10.48084/etasr.8702
J. Allen, ''Photoplethysmography and its application in clinical physiological measurement," Physiological Measurement, vol. 28, no. 3, Oct. 2007, Art. no. R1. DOI: https://doi.org/10.1088/0967-3334/28/3/R01
M. Elgendi et al., ''The use of photoplethysmography for assessing hypertension,'' NPJ Digital Medicine, vol. 2, no. 1, June 2019, Art. no. 60. DOI: https://doi.org/10.1038/s41746-019-0136-7
P. H. Charlton, P. A. Kyriacou, J. Mant, V. Marozas, P. Chowienczyk, and J. Alastruey, ''Wearable Photoplethysmography for Cardiovascular Monitoring,'' Proceedings of the IEEE, vol. 110, no. 3, pp. 355–381, Mar. 2022. DOI: https://doi.org/10.1109/JPROC.2022.3149785
R. A. Pinto, H. S. De Oliveira, E. Souto, R. Giusti, and R. Veras, ''Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network,'' Sensors, vol. 24, no. 18, Jan. 2024, Art. no. 6046. DOI: https://doi.org/10.3390/s24186046
Q. Tang et al., ''Robust Reconstruction of Electrocardiogram Using Photoplethysmography: A Subject-Based Model,'' Frontiers in Physiology, vol. 13, Apr. 2022, Art. no. 859763. DOI: https://doi.org/10.3389/fphys.2022.859763
L. Berger, M. Haberbusch, and F. Moscato, ''Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges,'' Artificial Intelligence in Medicine, vol. 143, Sept. 2023, Art. no. 102632. DOI: https://doi.org/10.1016/j.artmed.2023.102632
T. Golany and K. Radinsky, ''PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification,'' Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 557–564, July 2019. DOI: https://doi.org/10.1609/aaai.v33i01.3301557
Y. Lin et al., ''Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model,'' in 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, Sept. 2024, pp. 3505–3508. DOI: https://doi.org/10.1109/BIBM62325.2024.10821878
T. Du, J. Wang, R. Qu, and Z. Zhu, ''DS4U-Net: A Diffusion Model-Based Approach to Multi-Lead ECG Signal Generation,'' in 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), Apr. 2024, Xian, China, pp. 867–871. DOI: https://doi.org/10.1109/ICSP62122.2024.10743556
R. Banerjee, A. Sinha, A. D. Choudhury, and A. Visvanathan, ''PhotoECG: Photoplethysmographyto estimate ECG parameters,'' in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Feb. 2014, Florence, Italy, pp. 4404–4408. DOI: https://doi.org/10.1109/ICASSP.2014.6854434
Q. Zhu, X. Tian, C. W. Wong, and M. Wu, ''Learning Your Heart Actions From Pulse: ECG Waveform Reconstruction From PPG,'' IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16734–16748, Sept. 2021. DOI: https://doi.org/10.1109/JIOT.2021.3097946
X. Tian, Q. Zhu, Y. Li, and M. Wu, ''Cross-Domain Joint Dictionary Learning for ECG Reconstruction from PPG,'' in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Feb. 2020, Barcelona, Spain, pp. 936–940. DOI: https://doi.org/10.1109/ICASSP40776.2020.9054242
A. Ezzat, O. A. Omer, U. S. Mohamed, and A. S. Mubarak, ''ECG signal reconstruction from PPG using a hybrid attention-based deep learning network,'' EURASIP Journal on Advances in Signal Processing, vol. 2024, no. 1, Nov. 2024, Art. no. 95. DOI: https://doi.org/10.1186/s13634-024-01158-8
N. Murmu, R. Gupta, and K. Das Sharma, ''Real-Time PPG-to-ECG Reconstruction Model With On-Device Recalibration Facility,'' IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–9, 2024. DOI: https://doi.org/10.1109/TIM.2024.3450120
V. Bijalwan, V. B. Semwal, G. Singh, and T. K. Mandal, ''HDL-PSR: Modelling Spatio-Temporal Features Using Hybrid Deep Learning Approach for Post-Stroke Rehabilitation,'' Neural Processing Letters, vol. 55, no. 1, pp. 279–298, Feb. 2023. DOI: https://doi.org/10.1007/s11063-022-10744-6
K. Vo, M. El-Khamy, and Y. Choi, ''PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling,'' in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, July 2024, pp. 1–7. DOI: https://doi.org/10.1109/EMBC53108.2024.10781630
Q. Tang, Z. Chen, R. Ward, C. Menon, and M. Elgendi, ''PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments,'' Bioengineering, vol. 10, no. 6, June 2023, Art. no. 630. DOI: https://doi.org/10.3390/bioengineering10060630
E. Lan, "Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection," in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, Jan. 2023, pp. 1990–1998, https://doi.org/10.1109/WACV56688.2023.00203. DOI: https://doi.org/10.1109/WACV56688.2023.00203
K. Dashdondov and M. H. Kim, ''Mahalanobis Distance Based Multivariate Outlier Detection to Improve Performance of Hypertension Prediction,'' Neural Processing Letters, vol. 55, no. 1, pp. 265–277, Feb. 2023. DOI: https://doi.org/10.1007/s11063-021-10663-y
M. Saeed et al., ''Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*,'' Critical Care Medicine, vol. 39, no. 5, May 2011, Art. no. 952. DOI: https://doi.org/10.1097/CCM.0b013e31820a92c6
H. C. Lee, Y. Park, S. B. Yoon, S. M. Yang, D. Park, and C. W. Jung, ''VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients," Scientific Data, vol. 9, no. 1, June 2022, Art. no. 279. DOI: https://doi.org/10.1038/s41597-022-01411-5
A. E. W. Johnson et al., ''MIMIC-III, a freely accessible critical care database,'' Scientific Data, vol. 3, no. 1, May 2016, Art. no. 160035. DOI: https://doi.org/10.1038/sdata.2016.35
Y. Guo, Q. Tang, S. Li, and Z. Chen, ''Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network,'' Bioengineering, vol. 11, no. 4, Apr. 2024, Art. no. 365. DOI: https://doi.org/10.3390/bioengineering11040365
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