*4.1. Experimental Setup*

To assess the performance of our method, we classified 15 and 7 different types of heartbeats from the MIT-BIH and INCART databases, respectively. One major obstacle of our databases is that they are not well balanced. For instance, the normal types are over-represented while the supraventricular escape beats from INCART have few samples in comparison. To address this issue, we randomly sampled (without repetition), as in [20], 3350 heartbeats from the MIT-BIH database and 1720 heartbeats from INCART, in the proportions given by Tables 1 and 2 respectively.

We used k-fold cross validation on the resampled heartbeats to fit the parameters of 1-D CCANet-SVD. The parameters are shown in Table 4.


**Table 4.** Parameters for 1-D CCANet-SVD.

The results provided in the *Results and discussion* subsection derive from an overall confusion matrix obtained after summing the k confusion matrices given after each fold. As in [20], we performed 10 and 5-cross validation for the data from the MIT-BIH and INCART databases, respectively.

The code is written in Python 3.7 and we ran all the experiments on a personal computer equipped with Ubuntu 18.04. The hardware specifications of the computer are the following: 16 GB RAM, and i7-7700 CPU with a clock speed of 3.60 GHz.
