Classification of Heart Sounds Using Grey Level Co-occurrence Matrix and Logistic Regression

The heart is a person's fundamental organ.Heart sounds can help support healthcare workers by aiding in the early diagnosis of irregular heart rhythms.This study developed a system to categorize heart sounds by employing logistic regression as the classifier and the grey-level co-occurrence matrix as the classifier.For this reason, the GLCM technique was assessed in this work for feature extraction in the heart sound categorization.Moreover, the diagnostic heart sound analysis and classification procedure can be greatly improved by visualizing heart sounds using the Grey Level Co-occurrence Matrix (GLCM).The three data classifications for heart sounds are artifact, murmurs, and normal.Moreover, the heart sound is converted into the timefrequency domain using the short-time Fourier transform (STFT).The gray-level cooccurrence matrix approach is a useful tool for extracting the energy distribution in STFT.Dissimilarity, correlation, homogeneity, contrast, energy, and angular second moment (ASM) are the characteristics of the GLCM extraction.With dissimilarity offering the most feature extraction, logistic regression yields an 82% classification accuracy.The AUC value of 0.7 for the murmur class indicated that the feature and classification model had reduced sensitivity, but it performed well for the normal and artifact classes.This is because there are too few datasets for the murmur class.More abnormal class datasets are hoped to be contributed in the future in order to improve the classifier model.