Abstract
In this study the heartbeat sound signals were tackled by classifying them into heart disease categories such as normal, artifact, murmur and extrahals in an attempt for early detection of heart defects. Phonocardiogram (i.e., PCG) is used to obtain the digital recording dataset of the heart sounds using an electronic stethoscope or mobile device. Multiple features are extracted from the digital recording dataset such as MFCC, Delta MFCC, FBANK and a combination between MFCC and FBANK features. Moreover, to classify the heartbeat sound signals, multiple well-known machine learning classifiers were used such as Naive Bays (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The evaluation processes went through five metrics: Confusion matrix, accuracy, F1 score, precision and recall evaluating the recognition rate. Comparative experimental results show that the correctness of the feature with a best accuracy 99.2% adopted by MFCC and FBANK combination features which reduce false detection.
Highlights
Heart disease one of the most common reasons for increasing the number of deaths (Sowmiya and Sumitra, 2017)
The new method uses a simple method emulate some of the machine learning algorithms such as Support Vector Machines (i.e., SVM), Decision Tree (i.e., DT), Random Forest (i.e., RF), K-Nearest Neighbors (i.e., KNN), Artificial Neural Network (i.e., ANN) and Naive Bayes (i.e., Naive Bays (NB))
Heartbeat sound signals classification is tackled in this study into heart disease categories such as normal, artifact, murmur and extrahals
Summary
Heart disease one of the most common reasons for increasing the number of deaths (Sowmiya and Sumitra, 2017). Heart sound is the most sensitive indicator that classify the state of the heart (i.e., innocent, or abnormal). For a robust automatic detection of the heart defect in sound signals, there is a need for a strong Machine Learning (i.e., ML) algorithm to be able to recognize common features among different heart sound signals (i.e., innocent, or abnormal) (Gharaibeh et al, 2018; Ottom et al, 2019). An investigation aboutthe best method for classifying heart sound signals by combining sound features with well known ML algorithms.
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