Deep Learning-Driven Localization of Coronary Artery Stenosis Using Combined Electrocardiograms (ECGs) and Photoplethysmograph (PPG) Signal Analysis

The application of artificial intelligence (AI) to electrocardiograms (ECGs) and photoplethysmograph (PPG) for diagnosing significant coronary artery disease (CAD) is not well established. This study aimed to determine whether the combination of ECG and PPG signals could accurately identify the location of blocked coronary arteries in CAD patients. Simultaneous measurement of ECG and PPG signal data were collected from a Malaysian university hospital, including patients with confirmed significant CAD based on invasive coronary angiography. ECG and PPG datasets were concatenated to form a single dataset, thereby enhancing the information available for the training process. Experimental results demonstrate that the Convolutional Neural Networks (CNN) + Long Short-Term Memory (LSTM) + Attention (ATTN) mechanisms model significantly outperforms standalone CNN and CNN + LSTM models, achieving an accuracy of 98.12% and perfect Area Under the Curve (AUC) scores of 1.00 for the detection of blockages in the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA). The integration of LSTM layers captures temporal dependencies in the sequential data, while the attention mechanism selectively highlights the most relevant signal features. This study demonstrates that AI-enhanced models can effectively analyze simultaneous measurement of standard single-lead ECGs and PPG to predict the location of coronary artery blockages and could be a valuable screening tool for detecting coronary artery obstructions, potentially enabling their use in routine health checks and in identifying patients at high risk for future coronary events.

@article{Yid2024,
  title     = {Deep Learning-Driven Localization of Coronary Artery Stenosis Using Combined Electrocardiograms (ECGs) and Photoplethysmograph (PPG) Signal Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohd Syazwan Md Yid and Rosmina Jaafar and Noor Hasmiza Harun and Mohd Zubir Suboh and Mohd Shawal Faizal Mohamad},
  doi       = {10.14569/IJACSA.2024.0150974},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150974}
}