Biomedical Signal-to-Image Representations for Artificial Intelligence: A Review
Keywords:
Artificial intelligence, Biomedical signals, Deep learning, Image Representation, Time-frequency analysisAbstract
Biomedical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG), Surface Electromyogram (sEMG), Electromyogram (EMG), Electromyotomogram (EMT), and Photoplethysmography (PPG) are among the most commonly used physiological signals. They provide useful information for diagnosis and monitoring, rehabilitation, and wearable healthcare. However, these signals are not easily analyzed directly because physiological recordings are often non-stationary, nonlinear, noisy, and variable across different subjects and recording instances. This has led to the development of a large number of approaches that transform one-dimensional signals into two-dimensional representations, which can be processed using image-based artificial intelligence. This review discusses spectrograms, scalograms, EMD and Hilbert–Huang representations, recurrence plots, Gramian Angular Fields, and direct image encodings. Their suitability is evaluated with respect to the information they provide and the requirements of downstream learning tasks. The review also compares CNNs, transfer learning, hybrid CNN–recurrent neural networks, and LSTMs, as well as attention networks, Transformers, Vision Transformers, and self-supervised learning. Key drawbacks are transformation sensitivity, domain shifts, inconsistent evaluation protocols, computational expense, and poor interpretability. The review thus focuses on the co-selection of signal representation and AI architecture. Future research should look into adaptive, data-efficient multi-modal explainable edge-oriented architectures and explore potential paths for further research.
References
H. Witte and M. Wacker, “Time-Frequency Techniques in Biomedical Signal Analysis,” Methods of Information in Medicine, vol. 52, no. 04, pp. 279–296, 2013.
S. B. Shuvo et al., “Application of Wavelet Transformation and Artificial Intelligence Techniques in Healthcare: A Systemic Review,” WIREs Data Mining and Knowledge Discovery, vol. 15, no. 2, Mar. 2025.
D. Cvetkovic, E. D. Übeyli, and I. Cosic, “Wavelet Transform Feature Extraction from Human PPG, ECG, and EEG Signal Responses to ELF PEMF Exposures: A Pilot Study,” Digital Signal Processing, vol. 18, no. 5, pp. 861–874, Sep. 2008.
J. Rafiee, M. P. Schoen, N. Prause, A. Urfer, and M. A. Rafiee, “A Comparison of Forearm EMG and Psychophysical EEG Signals Using Statistical Signal Processing,” 2009 2nd International Conference on Computer, Control and Communication, pp. 1–5, Feb. 2009.
A. Shankar, H. K. Khaing, S. Dandapat, and S. Barma, “Analysis of Epileptic Seizures Based on EEG Using Recurrence Plot Images and Deep Learning,” Biomedical Signal Processing and Control, vol. 69, p. 102854, Aug. 2021.
Y. Elmir, Y. Himeur, and A. Amira, “ECG Classification Using Deep CNN and Gramian Angular Field,” 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 137–141, Jul. 2023.
Y. J. Jeon, C. Lee, and S. J. Kang, “Recurrence Plot Based Person Identification with ECG Using CNN Model,” 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 398–400, Jul. 2023.
Z. Ahmad and N. Khan, “Stress Classification from ECG Signals Using Vision Transformer,” IEEE Journal of Selected Areas in Sensors, vol. 3, pp. 47–56, 2026.
Pankaj, A. Kumar, M. Kumar, and R. Komaragiri, “Optimized Deep Neural Network Models for Blood Pressure Classification Using Fourier Analysis-Based Time–Frequency Spectrogram of Photoplethysmography Signal,” Biomedical Engineering Letters, vol. 13, no. 4, pp. 739–750, Jun. 2023.
G. C. Jana, R. Sharma, and A. Agrawal, “A 1D-CNN-Spectrogram Based Approach for Seizure Detection from EEG Signal,” Procedia Computer Science, vol. 167, pp. 403–412, 2020.
C. Qingzheng, T. Qing, and Z. Muchao, “CNN-based Gesture Recognition Using Raw Numerical Gray-Scale Images of Surface Electromyography,” Biomedical Signal Processing and Control, vol. 101, Mar. 2025.
A. Al Fahoum, A. Al Omari, G. Al Omari, and A. Zyout, “Development of a Novel Light-Sensitive PPG Model Using PPG Scalograms and PPG-NET Learning for Non-Invasive Hypertension Monitoring,” Heliyon, vol. 10, no. 21, Nov. 2024.
S. Y. Sen and N. Ozkurt, “ECG Arrhythmia Classification Using Convolutional Neural Network and Spectrogram,” 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–6, Oct. 2019.
J. Huang, B. Chen, B. Yao, and W. He, “ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network,” IEEE Access, vol. 7, pp. 92871–92880, 2019.
P. Biswas, S. Sana, and N. Z. Bushra, “ECG Signal Classification Using Continuous Wavelet Transform and Deep Neural Network,” 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–5, Feb. 2025.
Ö. Türk and M. S. Özerdem, “Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals,” Brain Sciences, vol. 9, no. 5, p. 115, May. 2019.
K. Kumar, K. Gupta, M. Sharma, V. Bajaj, and U. Rajendra Acharya, “INSOMNet: Automated Insomnia Detection Using Scalogram and Deep Neural Networks with ECG Signals,” Medical Engineering & Physics, vol. 119, no. 1, Jul. 2023.
Y.-H. Byeon, S.-B. Pan, and K.-C. Kwak, “Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics,” Sensors, vol. 19, no. 4, pp. 935, Feb. 2019.
F. Yousefi Rizi, “A Review of Notable Studies on Using Empirical Mode Decomposition for Biomedical Signal and Image Processing,” Signal Processing and Renewable Energy (SPRE), vol. 3, no. 4, pp. 89–113, Dec. 2019.
V. V. Boronoev and V. D. Ompokov, “The Hilbert-Huang Transform for Biomedical Signals Processing,” 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA), pp. 21–22, Jun. 2014.
B. Omarov, “Deep Learning in Biomedical Image and Signal Processing: A Survey,” Computers, Materials and Continua, vol. 85, no. 2, pp. 2195–2253, Sep. 2025.
A. M. Alqudah and Z. Moussavi, “A Review of Deep Learning for Biomedical Signals: Current Applications, Advancements, Future Prospects, Interpretation, and Challenges,” Computers, Materials & Continua/Computers, Materials & Continua (Print), vol. 0, no. 0, pp. 1–10, Jan. 2025.
A. Anwar, K. Yassin, J. L. Coyle, and E. Sejdic, “Transformers in Biosignal Analysis: A Review,” Information Fusion, vol. 114, Feb. 2025.
C. Wu and C. Ding, “Self-Supervised Learning for Biomedical Signal Processing: A Systematic Review on ECG and PPG Signals,” medRxiv, Oct. 2024.
X. Huang, H. Meng, and Z. Li, “Deep Learning for Epileptic Seizure Prediction from EEG Signals: A Review,” Biomedical Signal Processing and Control, vol. 117, Jan. 2026.
