## 2 Previous Research  
[PERSON] et al. [19] extracted features in the time domain including heart rate and waveform descriptors of the ECG signal on both leads available in the MITBIH Arrhythmia Database. They used a linear discriminant classifier that outperforms their past work at that time. [PERSON] et al. [20] presented a global classifier using the same training set as in [PERSON] et al. [19] but included two more databases to further test their method. He used the heart rate and features extracted from discrete wavelet transforms. He tested various sets of features and two different Bayesian linear classifiers ending with the linear discriminant classifier. [PERSON] et al. [21] modified their past work to include more than two leads, using the same features and classifier. He used the INCART database in which every record has 12 leads. Using all the leads available, he outperformed their past work using this database. This is the only database available with 12 leads. [PERSON] et al. [22] classified all types of heartbeats in the MITBIH database using the AAMI classification scheme. The morphological features used are coefficients of DWT and the result of applying independent components analysis, reducing the dimensionality with principal components analysis. The heart rate was also used as a feature. The classifier used is a support vector machine that combines the two leads of the MITBIH database. Once the features are extracted, the patients' heartbeats are used for training and testing resulting in overoptimistic results of nearly 100%. However, the same model trained and tested as [PERSON] et al. [19] and [PERSON] et al. [20; 21] produced lower performance because of the variabilities among patients. Their results are comparable to [PERSON] et al. [19] and [PERSON] et al. [20; 21]. [PERSON] et al. [23] principally worked on feature selection to classify ECG signals. They used a number of features in the temporal domain, morphological waveform features, statistical  
Figure 1: A supraventricular origin beat shows a narrow QRS (**left**) and a ventricular origin beat appears to be similar to a broad QRS (**right**). features, and features from the temporal-frequency domain using the DWT. The sequential forward floating search algorithm was used to determine relevant features, along with a linear discriminant classifier. Once the optimal features are extracted, they are used to classify through a multilayer perceptron. The results are comparable to [PERSON] et al. (2019), [PERSON] et al. (2020, 2011), and [PERSON] et al. (2020). It is mentioned in ([PERSON] et al., 2019) that general classification models are highly unreliable with ECG signals and are not widely used in practice. In order to overcome this problem faced by the intra-patient methodology, an adaptive patient model may be more effective because the patterns within a single patient are much easier to capture than in the inter-patient methodology. Such work involves the use of a certain number of beats previously labeled by a physician to train a learning algorithm. Works presented in ([PERSON] et al., 2019; [PERSON] et al., 2019) obtain results above 95% in almost all the cases, and some very close to 100%, in both precision and recall for the AAMI types of heartbeats. Only [PERSON] et al. (2019) and [PERSON] et al. (2019) showed results without initial expert labeling. [PERSON] et al. (2019) tested their algorithm developed using active learning with the same patients' beats against the software from HAMILTON and with the advantage that it does not require data from the patient, but it only recognizes premature ventricular beats. [PERSON] et al. (2019) outperformed HAMILTON. [PERSON] et al. (2019) modified their previous work in ([PERSON] et al., 2019), which uses a general classifier by including a clustering algorithm, the K-means method. This modification slightly improves the performance, compared to their previous work, and also allows an expert to contribute to the output of the algorithm.