Title:ECG for Cardiovascular Diseases Using Soft Computing Algorithms
Volume: 17
Issue: 3
Author(s): Prerak Mathur, Tanu Sharma and Karan Veer*
Affiliation:
- Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar
Punjab, 144027, India
Keywords:
Electrocardiogram, noise, filtering, QRS complex, machine learning, deep learning.
Abstract: Electrocardiogram (ECG) is widely used in the healthcare domain because of its usage
as a diagnostics tool for several cardiovascular diseases. It becomes essential to study and analyse
the ECG data with the help of classification techniques. In this review paper, a brief overview of
ECG signal information is presented. Various approaches for diagnosing cardiovascular diseases
have been discussed, along with the need for accurate ECG signal analysis. These approaches are
mainly based on the principles of machine learning and deep learning. The advantages and limitations
of these techniques in the detection of cardiovascular diseases are presented within the scope
of future work. This study can be helpful for researchers in bridging the gap between current approaches
and future techniques for the detection of arrhythmia conditions.