**5. Conclusions**

In this paper, we propose a novel heartbeat classification method based mainly on a new approach to the study of the correlation between the two ECG leads, to extract complex features. Our method also employ elementary hand-crafted time domain features, frequency domain features with a one-dimensional approach to spectrograms, and autoregressive coefficients. Our method is one-dimensional, allowing to explore a more complex neural architecture while maintaining a reasonable computational complexity, and providing better results. Our final model has an optimal structure and performs the classification of 15 and 7 heartbeat types for the MIT-BIH and INCART databases, respectively. Finally, our method outperforms [20] with a slightly better overall accuracy and mean ppv on the MIT-BIH database and a notably higher overall accuracy (+1.69%), mean sensitivity (+2.89%), and mean ppv (+1.78%) on the INCART database.

**Author Contributions:** All the authors contributed equally to the conceptualization; all the authors contributed equally to the methodology; all the authors contributed equally to the software; all the authors contributed equally to the validation; all the authors contributed equally to the draft preparation; all the authors contributed equally to the writing, review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
