**6. Conclusions**

In this chapter, we develop a VAE model to recognize a tiny distortion on ECG signals. First, we analyze the characteristics of the features of the ECG signals, which are closely related to ECG components such as P-waves, QRS complex, and T-waves. Second, we explain an algorithm that deals with the location of R peaks. On the basis of the algorithm, we abstract a segment of ECG signal between two adjacent R peaks from three real-life ECG databases. Finally, we train our models by using the selected ECG signals. The results of our experiments demonstrate that the proposed VAE model can be used as an effective tool to automatically recognize ECG signals. Especially, this model is robust to some kinds of noises that are usually produced during the sampling procedures. Furthermore, as a generative model, VAE is a recently established based on the neural networks. The important characteristic of the model is that it can be used in the scenario of the unsupervised learning [31]. Simultaneously, with the emergence of the large amount of unlabeled ECG records and the requirement for real-time diagnosis of heart illness by automatic recognition ECG signals, our method in this chapter can offer a solution to these problems.

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In the view of the clinic, future work should put more energy on setting up the set of features of ECG signals, especially, the relationship between the features and the heart diseases. Additionally, because of the physiological characteristics of heart, a single ECG wave may not accurately represent the entire situation of the heart, it is therefore desirable to obtain all of ECG signals from all of 12 or 18 leads. For example, if an anterior wall myocardial infarction happens. Feature of ST-segment elevation reciprocally changes on the ECGs from the leads of I, aVL, and V1–V5. Therefore, the general implementation of VAE model to such clinic situations warrants further study.
