A Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

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Pedro Henrique Borghi


Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour.

This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.


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Author Biography

Pedro Henrique Borghi, University of Porto; IP Bragança; Federal University of Technology - Paraná

Faculty of Engineering

University of Porto

Rua Dr. Roberto Frias

4200-465 PORTO



Research Centre in Digitalization and Intelligent Robotics (CEDRI)

UNIAG, Instituto Politécnico de Bragança

5300-253 BRAGANÇA



Federal University of Technology - Paraná (UTFPR)

Av. Alberto Carazzai, 1640