  The heart is one of the most vital organs in the human body. It supplies
blood and nutrients in other parts of the body. Therefore, maintaining a
healthy heart is essential. As a heart disorder, arrhythmia is a condition in
which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is
used to analyze the arrhythmia problem from the ECG signals because of its
fewer difficulties and cheapness. The heart peaks shown in the ECG graph are
used to detect heart diseases, and the R peak is used to analyze arrhythmia
disease. Arrhythmia is grouped into two groups - Tachycardia and Bradycardia
for detection. In this paper, we discussed many different techniques such as
Deep CNNs, LSTM, SVM, NN classifier, Wavelet, TQWT, etc., that have been used
for detecting arrhythmia using various datasets throughout the previous decade.
This work shows the analysis of some arrhythmia classification on the ECG
dataset. Here, Data preprocessing, feature extraction, classification processes
were applied on most research work and achieved better performance for
classifying ECG signals to detect arrhythmia. Automatic arrhythmia detection
can help cardiologists make the right decisions immediately to save human life.
In addition, this research presents various previous research limitations with
some challenges in detecting arrhythmia that will help in future research.
