Heart disease is the principal cause of death across the globe and the ECG signals are used to diagnose it. The correct classification of this disease allows us the opportunity to apply a more focused treatment. ECG signals are fed into Automated Diagnosis Systems, and these systems use techniques like processing digital signals, machine learning, and deep learning. This paper shows the results when the sampling frequency of the ECG signals is resampled and proposes a new preprocessing stage. The new stage applies Wavelet based on Atomic Functions to eliminate the noise and baseline wander. The Wavelet based on Atomic Functions have demonstrated successful performances in computer science. The ECG signals are segmented into 1, 2, 5, and 10 s; these segmented signals are fed into the classifier stage. Our proposal was tested in four accessible public databases separately, and finally by gathering the databases. We were able to successfully differentiate between 11 types of ECG signals with an accuracy of 98.9%.

心脏病是全球死亡的主要原因，心电图信号可用于诊断。对这种疾病的正确分类使我们有机会进行更集中的治疗。ECG信号被馈入自动诊断系统，这些系统使用诸如处理数字信号，机器学习和深度学习之类的技术。本文显示了对ECG信号的采样频率进行重新采样时的结果，并提出了一个新的预处理阶段。新阶段将基于原子函数的小波应用于消除噪声和基线漂移。基于原子函数的小波已经证明了在计算机科学中的成功表现。ECG信号分为1、2、5和10 s；这些分段的信号被馈送到分类器级。我们的建议已分别在四个可访问的公共数据库中进行了测试，最后通过收集数据库进行了测试。我们能够成功区分11种类型的ECG信号，准确性为98.9％。