## 3 Experiment Setup  
### Database of ECG Signals  
The AAMI is an organization that establishes guidelines and standards for the automatic classification of ECG signals (ANSI/AAMI, 2020) [25]. They have developed a set of recommendations known as the AAMI Guidelines for the Classification of Electrocardiograms (ANSI/AAMI EC57), which provides guidance on terminology, performance criteria and evaluation metrics for automated cardiac arrhythmia classification systems. The AAMI guidelines define five main classes of arrhythmias widely used in the automatic classification of ECG signals: normal (N), supraventricular (S), ventricular (V), fusion of normal and ventricular (F) and unknown (Q). These guidelines are important, as they standardize the terminology and approach, allowing for a comparison of results between different automatic classification systems that have been proposed in the literature. This standardization also contributes to the development of more reliable systems for cardiac arrhythmia classification. The ANSI/AAMI standard [25] emphasizes the importance of high-quality data for the development and evaluation of arrhythmia classification algorithms and recommends the use of certain public databases. Among the recommended databases is the MIT-BIH dataset (2005), which was used in this study to develop and test the proposed structure, considering both the interpatient and intrapatient approaches. The MIT-BIH database has been widely used for the development and validation of automated classification models of ECG signals and is an important reference for the scientific community in the performance evaluation and comparison of different methods. This database contains 48 recordings of ECG signals from 47 patients that are categorized into various classes that represent different types of arrhythmias and cardiac conditions. The patients range in age from 32 to 89 years, and the data are recorded at a sampling frequency of 360 Hz, totaling approximately 30 min of ECG signal measurements per patient. Table 2 presents the organization of the annotations and classes in the MIT-BIH database. For the ECG signal leads, the MIT-BIH database [41] contains signals obtained from the MLII and V leads, with the MLII lead being the most commonly used for the development of the arrhythmia classifiers presented in the state-of-the-art studies, which were also used in our work. Regarding the signals obtained from the V lead, from the results presented by [15; 16], it was concluded that classification using the V and MLII leads for classification could lead to improved results. The signals from the V lead are obtained from electrodes placed on the chest, which record the action potentials in the ventricular muscles, which may favor the classification of class V (ventricular) arrhythmias [42]. For this case, two segments of ECG signals are used as inputs for the CNN-MA structure (data segment of the MLII lead and data segment of the V lead), and, for each ECG segment, two images (HSFC and RP) are generated, which are then used as inputs for the four multimodal convolutional layers. Preprocessing of the ECG Signal  
All original records from the MIT-BIH database were segmented into 2 s intervals, with the segmentation being based on the observation of the time required to capture at least two RR intervals per segment. After segmentation, the signals were cleaned using a technique based on visibility graphs, as described in a study by [43]. This technique was designed to highlight the R peaks and reduce signal noise. After the signals were cleaned, the baseline, which may have been generated by factors such as breathing or movement during patient data collection, was removed. Figure 5 shows an example with three segments obtained from a patient record after signal cleaning and baseline removal.