A Hybrid Deep Learning Approach for Coronary Artery Disease Detection and Severity Analysis Using ECG Signals
Received: 1 July 2025 | Revised: 28 July 2025 | Accepted: 15 August 2025 | Online: 5 September 2025
Corresponding author: Kushwant Kaur
Abstract
Cardiovascular diseases such as Coronary Artery Disease (CAD) are life-threatening. CAD can be detected in the early stages using Electrocardiogram (ECG) signals. However, ECG disturbances have a wide range of manifestations with varying clinical significance. This paper presents a deep learning-based technique for the detection of CAD using ECG signals. The proposed method addresses common signal quality issues by employing advanced preprocessing techniques, including elliptic (Cauer) filtering for baseline wander elimination, Chebyshev Type I filtering for power line interference removal, and adaptive comb filtering for electrode motion artifacts exclusion. The preprocessed signals are then standardized using z-score normalization. During signal segmentation (one beat in each segment), the proposed model eliminates false peaks based on an adaptive threshold with morphological and statistical evaluation. The proposed model integrates Bidirectional Long Short-Term Memory (Bi-LSTM) and Neural Basis Expansion Analysis for Time Series (N-BEATS) for feature extraction, dimensionality reduction, and classification. A clustering approach is used to not only enhance the accuracy of cluster formation but also improve the overall efficiency of the K-means algorithm. By integrating the dynamic characteristics of horse herd behavior, the method adapts to varying data distributions, leading to more robust clustering results. After CAD detection, its severity is categorized using the Minnesota Code (MC) by analyzing QRS voltage, ST elevation, ST depression, and T-wave inversion patterns on different ECG leads. The proposed system was evaluated using accuracy, specificity, sensitivity, and F1-score, showing that it has the potential to aid clinicians in the detection of CAD in the initial stages using ECG signals.
Keywords:
coronary artery disease, ECG signal, segmentation, deep learning, feature extraction, baseline wanderDownloads
References
T. Qi, H. Zhang, H. Zhao, C. Shen, and X. Liu, "Research on ECG Signal Classification Based on Hybrid Residual Network," Applied Sciences, vol. 14, no. 23, Dec. 2024, Art. no. 11202.
I. D. Mienye and Y. Sun, "Improved Heart Disease Prediction Using Particle Swarm Optimization Based Stacked Sparse Autoencoder," Electronics, vol. 10, no. 19, Sep. 2021, Art. no. 2347.
S. Deivanayagi and P. S. Periasamy, "Computer Aided Coronary Atherosclerosis Plaque Detection and Classification," Intelligent Automation & Soft Computing, vol. 34, no. 1, pp. 639–653, 2022.
H. Zhang et al., "Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy," Entropy, vol. 23, no. 7, Jun. 2021, Art. no. 823.
L. Yao et al., "Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted From QT Interval Time Series and ST–T Waveform," IEEE Access, vol. 8, pp. 129510–129524, 2020.
L. Xie, Z. Li, Y. Zhou, Y. He, and J. Zhu, "Computational Diagnostic Techniques for Electrocardiogram Signal Analysis," Sensors, vol. 20, no. 21, Nov. 2020, Art. no. 6318.
A. Darmawahyuni et al., "Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier," Algorithms, vol. 12, no. 6, Jun. 2019, Art. no. 118.
Z. Ebrahimi, M. Loni, M. Daneshtalab, and A. Gharehbaghi, "A review on deep learning methods for ECG arrhythmia classification," Expert Systems with Applications: X, vol. 7, Sep. 2020, Art. no. 100033.
R. Banerjee, A. Ghose, and K. Muthana Mandana, "A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG," in 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, Jul. 2020, pp. 1–8.
J. Rymko, M. Solinski, A. Perka, J. Rosinski, and M. Lepek, "Classification of Atrial Fibrillation in Short-term ECG Recordings Using a Machine Learning Approach and Hybrid QRS Detection," presented at the 2017 Computing in Cardiology Conference, Sep. 2017.
H. J. Suleiman, I. R. A. Hamid, and O. R. Olaniran, "Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 22565–22572, Jun. 2025.
A. Hernandez-Matamoros, H. Fujita, E. Escamilla-Hernandez, H. Perez-Meana, and M. Nakano-Miyatake, "Recognition of ECG signals using wavelet based on atomic functions," Biocybernetics and Biomedical Engineering, vol. 40, no. 2, pp. 803–814, Apr. 2020.
K. Kaur and G. Bathla, "Deep Learning Approach for Coronary Artery Disease Detection and Severity Analysis Using ECG Signals," in 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), Nagpur, India, Dec. 2024, pp. 1–6.
D. Moldovan, "Horse Optimization Algorithm: A Novel Bio-Inspired Algorithm for Solving Global Optimization Problems," in Artificial Intelligence and Bioinspired Computational Methods, vol. 1225, R. Silhavy, Ed. Springer International Publishing, 2020, pp. 195–209.
N. Emami and M. Kuchaki Rafsanjani, "Feature selection strategy based on hybrid horse herd optimization algorithm and perturbation theory: an mRMI approach," Annals of Operations Research, Nov. 2024.
B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, "N-BEATS: Neural basis expansion analysis for interpretable time series forecasting." arXiv, Feb. 20, 2020.
A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet," Circulation, vol. 101, no. 23, pp. e215–e220, Jun. 2000.
C. Sun et al., "Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals," Bioengineering, vol. 11, no. 11, Oct. 2024, Art. no. 1093.
O. S. Lih et al., "Comprehensive electrocardiographic diagnosis based on deep learning," Artificial Intelligence in Medicine, vol. 103, Mar. 2020, Art. no. 101789.
Downloads
How to Cite
License
Copyright (c) 2025 Kushwant Kaur, Gaurav Bathla
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the 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 able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
