The increasing prevalence of cardiovascular diseases (CVDs) has become a
major health concern. Arrhythmia is the deadliest heart condition of all
cardiovascular disorders. Thus, timely and precise arrhythmia diagnosis is
critical in preventing heart disease and abrupt cardiac death. Arrhythmia can be
discovered on an electrocardiogram (ECG) by observing irregular heart
electrical activity. The heart's electrical activity is recorded as an ECG signal,
which contains both normal and pathological information. Classification of ECG
patterns is critical for automatically diagnosing cardiac illness. This paper
discusses the various learning approaches for automatically distinguishing
different types of heartbeats. According to reported studies, the convolutional
neural network (CNN) model is the best option for classifying arrhythmia. An
ensemble of depth wise separable convolutional (DSC) neural networks
achieves the highest classification accuracy, 99.88%.
Keywords: Datasets,GA (Genetic algorithm), Feature selection, Information
Gain, Missing Values Imputation, RMSE (Root mean square error)
Missing Value Estimation Methods for Classification of Arrhythmia using Deep Learning: Review Study
This paper discusses the various learning approaches for automatically distinguishing different types of heartbeats. According to reported studies, the CNN model is the best option for classifying arrhythmia. An ensemble of DSC neural networks achieves the highest classification accuracy, 99.88%.