### Literature Review  
The following is a literature review of Machine Learning algorithms for the task of cardiac arrhythmia feature extraction and classification. ECG classification research can be divided into either heartbeat [11; 12; 13] or arrhythmia [5; 10; 12]. The most common algorithms included in studies are Support Vector Machine (SVM) [11; 12; 13; 14], ANN [12], and CNN [10; 14; 15; 16]. Large public databases of annotated ECG signals play a fundamental role in developing algorithms for automatic ECG interpretation and classification, serving as a benchmark for comparing the validation and quantitative evaluation of algorithms from different papers in the scientific community [2]. The frequent databases used are MIT-BIH [11; 12; 14; 16], TNMG [10], and cinc17 [5; 14; 15; 17]. Figure 1 shows the initial workflow of how researchers approached the classification task of ECG signals with SVM and ANN algorithms. The workflow starts with signal filtering and QRS and R-peak detection algorithms methods, followed by feature extraction and simple classification with a classifier or classification fusion methods with multiple classifiers. A broad set of handcrafted features for ECG analysis, such as temporal relationships between waves, morphological descriptors, state-space features, linear transform, spectral representation, wavelet analysis, etc., have been described as well [11; 12; 18; 19; 20; 21; 22; 23; 24]. Among the temporal features, a wide assortment of QRS morphological descriptors was mentioned, including QRS width, positive and negative peak amplitudes, QRS slopes, and cardiogram vector descriptors. As for spectral feature extractor methods, previous studies mention methods such as Fourier Organisation Analysis, which evaluates the harmonic distribution of the energy of the ECG waveform, high order spectra (HOS), Fourier Transform (FT), and Wavelet Transform (WT) methods (e.g., Short-term Fourier Transform (STFT)). Some approaches with a single feature cannot effectively face the complexity of an ECG signal. Therefore, many researchers resort to combining different feature extraction methods. In addition, understanding the specific characteristics of the pathophysiological conditions studied is advisable. For instance, if Premature Ventricular Contraction (PVC) detection is desired, morphological markers are essential since this arrhythmia occurs prematurely [2]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Database** & **Sampling** & **Number of** & **Leads** & **Number of** & **Pathology** \\
**Acronym** & **(Hz)** & **Records** & **Disorders** & **Annotation** \\ \hline CINC17 & 300 & 8528 & Lead I & 4 & Heart rhythms \\ TNMG & 400 & 827 & 12-Lead & 6 & Heart rhythms \\ \hline \end{tabular}
\end{table} 
Table 1: ECG Database Overview. Deep learning algorithms rely primarily on data representation learning techniques, meaning the signals do not necessarily need to be pre-processed. A current literature review on the implementation of CNN for the classification of myocardial infarction and arrhythmia has been presented by [PERSON] et al. (2022) [25] and [PERSON] et al. (2022) [26]. The literature reviews concluded that CNN algorithms have recently been the most widely used deep learning networks for arrhythmia classification. The ECG signals dataset can be given as a time series or images; with the latter being the most popular. Time-series signals could be used as input, obtaining not only QRS-complex information but also P and T-wave information, thereby obtaining a better representation of the signal [5; 10; 16; 27]. It does not imply that the raw ECG signal is the sole input of the model. Researchers have evaluated various input variables, such as spectrogram images of the ECG or even a wide variety of features calculated with signal processing methods mentioned previously [15; 28; 29; 30]. In this paper, the SVM classifier and 1D-CNN will be compared. The 1D-CNN architecture is the model presented by [PERSON] et al. [5] for arrhythmia classification.