Investigating the preprocessing methods in ECG analysis


Ekinci G., Kardeş E., Güvenkaya H., KARAGÖZ P.

29th Signal Processing and Communications Applications Conference (SIU), 09 June 2021 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu53274.2021.9477702
  • Keywords: ECG, Electrocardiogram, preprocessing, deep learning, machine learning, classification, anomaly detection, HEARTBEAT CLASSIFICATION, DATABASE
  • Middle East Technical University Affiliated: Yes

Abstract

The electrocardiogram (ECG) signals are basically the combination of sequential electrical impulses generated by tissues in the heart. In the last decades, by using ECG signals, various studies such as heart beat classification, arrhythmia analysis, anomaly detection, and diagnosis of heart-related diseases have been conducted with various neural network (NN), and machine learning (ML) approaches. Regardless of the approach to obtain more accurate results, various preprocessing methods are applied to data. It is observed that performing preprocessing is crucial for the sake of the related analysis. In this paper, focused on studies in the last decade, ECG analysis steps are briefly explained and mentioned approaches are reviewed on their preprocessing methods in detail.