The Cardio Twin CNN architecture for detecting Ischemic Heart Disease from ECGs achieved 85.77% accuracy with a 4.8-second classification time per sample on the edge.
A CNN-based digital twin architecture running on the edge can classify ischemic heart disease from ECGs with 85.77% accuracy.
We present the Cardio Twin architecture for Ischemic Heart Disease (IHD) detection designed to run on the edge. We classify non-myocardial and myocardial conditions with a CCN. This CNN generates features from the electrocardiograms and performs the classification task. The database used is "PTB Diagnostic ECG Database" from Physio Bank and it comes from 200 different people. Each patient data sample was partitioned into 2.5 second windows for training. The implemented model achieved 85.77% accuracy and used 4.8 seconds for each sample classification. The results show that technology is ready to fully support demanding processes, such as Digital Twin, on the edge.
Velazquez et al. (Sat,) conducted a other in Ischemic Heart Disease (IHD) (n=200). Cardio Twin architecture (CNN) was evaluated on Classification accuracy for non-myocardial and myocardial conditions. The Cardio Twin CNN architecture for detecting Ischemic Heart Disease from ECGs achieved 85.77% accuracy with a 4.8-second classification time per sample on the edge.