Fig. 1. Protocol overview.
a Data acquisition: We highlight the three primary subgroups of exercise stress testing: ① patients who complete the bicycle exercise stress test, ② patients not able to exercise on the bicycle, and for whom a pharmaceutical protocol is used at the beginning of the stress test, and ③ patients starting on the bicycle but need pharmacological support to reach their target heart rate. Doctors perform myocardial perfusion scans at rest (rest MPS), and at the target heart rate (stress MPS). Myocardial perfusion is quantified by the myocardial perfusion scan summed rest score (MPSSR score), and the MPS summed stress score (MPSSS score). The cardiologist estimates the probability of a functionally relevant CAD (fCAD) before and after the stress test (Pre/Post-Test CAD Probability in the figure). The binary label indicating the presence of fCAD (yellow box) is adjudicated by considering the stress test results and additional relevant clinical parameters. b Data Preprocessing: Following smoothing and outlier removal, time series that serve as input to the neural network are constructed by joining short subsequences from different phases of the stress test. For this, 2 s from the pre-stress phase, 6 s from the stress phase, and 2 s from the recovery phase are sampled and concatenated multiple times for a single patient (green, orange, and purple sequences). x-axes represent time in seconds. c Machine Learning: For our neural network approach (CARPEECG), these 2-6-2 sequences are fed into a residual neural network (ResNet). In parallel, the patient’s static clinical data are processed by a 2-layer feedforward network. Four subnetworks are trained on three auxiliary tasks (i.e., MPSSR & MPSSS score as well as stress type prediction) and one main task (fCAD prediction). We average predictions of the main task over all 2-6-2 sequences per patient. Purple arrows in front of each task indicate the direction of the learning signal. The same clinical variables as for CARPEECG are used to train a random forest classifier (CARPEClin.); nodes are coloured to enhance legibility. We combine both predictions with the cardiologist’s judgement in a logistic regression model (CARPEColl.).