Feature Extraction Techniques for Automatic Detection of Some Specific Cardiovascular Diseases Using ECG: A Review and Evaluation Study

Abstract

Cardiovascular diseases (CVDs) are the leading cause of death globally. For an effective treatment of CVDs, automatic CVDs detection based on Electrocardiograph (ECG) monitoring is highly desirable. One major challenge in ECG classification is feature learning. This paper reviews developed techniques for feature extraction and compared their performances in Atrial Fibrillation (AF), Myocardial Infarction (MI) and Pericarditis detection. Feature extraction methods in the literature reviews can be divided into mainly four categories: linear feature, nonlinear feature, wavelet transform, deep learning. Three studies were implemented using database from PhysioNet to evaluate the effectiveness of different feature extraction techniques. The AF detection algorithm use morphology features, statistic features, spectral feature, and wavelet entropy, presented a sensitivity of 96%, specificity of 93% and accuracy of 94.1%. In the case of Pericarditis and MI classification, information thoery-based features including subband energy, permutation entropy, and approximate entropy are the most noteworthy features. The study of detecting MI using machine learning based model of Convolutional Neural Network showed a sensitivity of 92.04% which yield the most promising results.