Sudden cardiac death (SCD) remains one of the leading causes of mortality worldwide, with coronary artery disease (CAD) as its predominant underlying condition. However, noninvasive and accessible screening approaches for CAD are still limited. This study aims to develop and evaluate a photoplethysmography (PPG)-based method for CAD detection using a two-dimensional Gramian angular field (GAF) transformation combined with deep learning. We enrolled 89 patients with CAD and 70 healthy controls and converted their PPG signals into two GAF representations—Gramian angular summation field (GASF) and Gramian angular difference field. The GASF representation, which preserves both magnitude and phase relationships within the PPG waveform, was found to provide superior discriminative capability. Using GASF as input, the proposed SE-ResNet model achieved an accuracy of 92.43% (95% CI: 91.51–93.36), outperforming prior work that reported 83.8% accuracy (95% CI: 82.2–85.3). These results demonstrate that the GAF transformation enhances CAD detection by encoding the temporal–phase dynamics of PPG signals, which are often overlooked in conventional one-dimensional analyses. The proposed GASF-SE-ResNet framework therefore shows strong potential as a noninvasive low-cost tool for CAD screening and SCD risk reduction.
| [1] |
Kolk MZ, Ruipérez-Campillo S, Wilde AA, Knops RE, Narayan SM, Tjong FV. Prediction of sudden cardiac death using artificial intelligence: current status and future directions. Heart Rhythm Off J Heart Rhythm Soc. 2024.
|
| [2] |
Empana JP, Lerner I, Valentin E, et al. Incidence of sudden cardiac death in the European union. J Am Coll Cardiol. 2022; 79(18): 1818-1827. https://doi.org/10.1016/j.jacc.2022.02.041
|
| [3] |
Marijon E, Narayanan K, Smith K, et al. The lancet commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action. Lancet. 2023; 402(10405): 883-936. https://doi.org/10.1016/s0140-6736(23)00875-9
|
| [4] |
Osman J, Tan SC, Lee PY, Low TY, Jamal R. Sudden cardiac death (SCD)–risk stratification and prediction with molecular biomarkers. J Biomed Sci. 2019; 26: 1-12. https://doi.org/10.1186/s12929-019-0535-8
|
| [5] |
Caffaratti H, Slater B, Shaheen N, et al. Neuromodulation with ultrasound: hypotheses on the directionality of effects and community resource. Neurology. 2024:2024.06.14.24308829. https://doi.org/10.1101/2024.06.14.24308829
|
| [6] |
Terentes-Printzios D, Oikonomou D, Gkini KP, et al. Angiography-based estimation of coronary physiology: a frame is worth a thousand words. Trends Cardiovasc Med. 2022; 32(6): 366-374. https://doi.org/10.1016/j.tcm.2021.07.004
|
| [7] |
Kwok CS, Burke H, McDermott S, et al. Missed opportunities in the diagnosis of heart failure: evaluation of pathways to determine sources of delay to specialist evaluation. Curr Heart Fail Rep. 2022; 19(4): 247-253. https://doi.org/10.1007/s11897-022-00551-4
|
| [8] |
Kazemi A, Shamsa A, Ghorshi S, Panahi I. Designing a convolutional neural network for classifying phonocardiogram signals for coronary artery disease detection. In: 2025 IEEE 18th Dallas Circuits and Systems Conference (DCAS); 2025: 1-5. https://doi.org/10.1109/DCAS65331.2025.11045465
|
| [9] |
Singh A, Nagabhooshanam N, Kumar R, et al. Deep learning based coronary artery disease detection and segmentation using ultrasound imaging with adaptive gated SCNN models. Biomed Signal Process Control. 2025; 105:107637. https://doi.org/10.1016/j.bspc.2025.107637
|
| [10] |
Han X, Pang J, Xu D, et al. Coronary artery disease severity and location detection using deep-mining-based magnetocardiography pattern features. Comput Methods Progr Biomed. 2025; 266:108764. https://doi.org/10.1016/j.cmpb.2025.108764
|
| [11] |
Banerjee R, Vempada R, Mandana K, Choudhury AD, Pal A. Identifying coronary artery disease from photoplethysmogram. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct; 2016: 1084-1088.
|
| [12] |
Paradkar N, Chowdhury SR. Coronary artery disease detection using photoplethysmography. In: 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (EMBC). IEEE; 2017: 100-103.
|
| [13] |
Elgendi M, Fletcher R, Liang Y, et al. The use of photoplethysmography for assessing hypertension. npj Digit Med. 2019; 2(1): 60. https://doi.org/10.1038/s41746-019-0136-7
|
| [14] |
Pignatelli N, Ma B, Sengputa S, Sengupta P, Mungulmare K, Fletcher RR. Low-cost mobile device for screening of atherosclerosis and coronary arterial disease. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2018: 5325-5328.
|
| [15] |
Al Fahoum AS, Abu Al-Haija AO, Alshraideh HA. Identification of coronary artery diseases using photoplethysmography signals and practical feature selection process. Bioengineering. 2023; 10(2):249. https://doi.org/10.3390/bioengineering10020249
|
| [16] |
Dash A, Jain K, Ghosh N, Patra A. Non-invasive detection of coronary artery disease from photoplethysmograph using lumped parameter modelling. Biomed Signal Process Control. 2022; 77:103781. https://doi.org/10.1016/j.bspc.2022.103781
|
| [17] |
Wu J, Liang H, Ding C, Huang X, Huang J, Peng Q. Improving the accuracy in classification of blood pressure from photoplethysmography using continuous wavelet transform and deep learning. Int J Hypertens. 2021; 2021(1): 9938584-9938589. https://doi.org/10.1155/2021/9938584
|
| [18] |
Al Fahoum A, Al Omari A, Al Omari G, Zyout A. Development of a novel light-sensitive PPG model using PPG scalograms and PPG-NET learning for non-invasive hypertension monitoring. Heliyon. 2024; 10(21):e39745. https://doi.org/10.1016/j.heliyon.2024.e39745
|
| [19] |
Allen J, Liu H, Iqbal S, Zheng D, Stansby G. Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study. Physiol Meas. 2021; 42(5):054002. https://doi.org/10.1088/1361-6579/abf9f3
|
| [20] |
Iqbal S, Agarwal S, Purcell I, Murray A, Bacardit J, Allen J. Deep learning identification of coronary artery disease from bilateral finger photoplethysmography sensing: a proof-of-concept study. Biomed Signal Process Control. 2023; 86:104993. https://doi.org/10.1016/j.bspc.2023.104993
|
| [21] |
Arts LP, Van den Broek EL. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis. Nat Comput Sci. 2022; 2(1): 47-58. https://doi.org/10.1038/s43588-021-00183-z
|
| [22] |
Knuuti J, Wijns W, Saraste A, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes: the task force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J. 2020; 41(3): 407-477. https://doi.org/10.1093/eurheartj/ehz425
|
| [23] |
Fihn SD, Gardin JM, Abrams J, et al. ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American college of cardiology foundation/american heart association task force on practice guidelines, and the American college of physicians, American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons. J Am Coll Cardiol. 2012; 60(24): e44-e164. https://doi.org/10.1016/j.jacc.2012.07.013
|
| [24] |
Parsaoran AJ, Mandala S, Pramudyo M. Study of denoising algorithms on photoplethysmograph (PPG) signals. In: 2022 International Conference on Data Science and Its Applications (ICoDSA). IEEE; 2022: 289-293.
|
| [25] |
Liang Y, Elgendi M, Chen Z, Ward R. An optimal filter for short photoplethysmogram signals. Sci Data. 2018; 5(1): 1-12. https://doi.org/10.1038/sdata.2018.76
|
| [26] |
Tsuchiya M, Yokoyama M. A simple method for evaluating the balance of the autonomic nervous system using photoplethysmography. Microsyst Technol. 2018; 24(1): 691-698. https://doi.org/10.1007/s00542-017-3388-7
|
| [27] |
Fu R, Zhang B, Liang H, Wang S, Wang Y, Li Z. Gesture recognition of sEMG signal based on GASF-LDA feature enhancement and adaptive ABC optimized SVM. Biomed Signal Process Control. 2023; 85:105104. https://doi.org/10.1016/j.bspc.2023.105104
|
| [28] |
Kucukler OF, Amira A, Malekmohamadi H. EEG channel selection using Gramian angular fields and spectrograms for energy data visualization. Eng Appl Artif Intell. 2024; 133:108305. https://doi.org/10.1016/j.engappai.2024.108305
|
| [29] |
Temko A. Accurate heart rate monitoring during physical exercises using PPG. IEEE Trans Biomed Eng. 2017; 64(9): 2016-2024. https://doi.org/10.1109/tbme.2017.2676243
|
| [30] |
Ray D, Collins T, Woolley SI, Ponnapalli PV. A review of wearable multi-wavelength photoplethysmography. IEEE Rev Biomed Eng. 2021; 16: 136-151. https://doi.org/10.1109/rbme.2021.3121476
|
| [31] |
Fan J, Wen J, Lai Z. Myoelectric pattern recognition using Gramian angular field and convolutional neural networks for muscle–computer interface. Sensors. 2023; 23(5):2715. https://doi.org/10.3390/s23052715
|
| [32] |
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016: 770-778.
|
| [33] |
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018: 7132-7141.
|
| [34] |
Shim M, Lee SH, Hwang HJ. Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection. Sci Rep. 2021; 11(1):7980. https://doi.org/10.1038/s41598-021-87157-3
|
| [35] |
Wahbah M, Mohandes B, EL-Fouly TH, El Moursi MS. Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling. Energy Convers Manag. 2022; 266:115811. https://doi.org/10.1016/j.enconman.2022.115811
|
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2025 The Author(s). Journal of Intelligent Medicine published by John Wiley & Sons Australia, Ltd on behalf of Tianjin University.
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