A New Method for Heart Disease Detection: Long Short-Term Feature Extraction from Heart Sound Data

Abstract

Heart sounds have been extensively studied for heart disease diagnosis for several decades. Traditional machine learning algorithms applied in the literature have typically partitioned heart sounds into small windows and employed feature extraction methods to classify samples. However, as there is no optimal window length that can effectively represent the entire signal, windows may not provide a sufficient representation of the underlying data. To address this issue, this study proposes a novel approach that integrates window-based features with features extracted from the entire signal, thereby improving the overall accuracy of traditional machine learning algorithms. Specifically, feature extraction is carried out using two different time scales. Short-term features are computed from five-second fragments of heart sound instances, whereas long-term features are extracted from the entire signal. The long-term features are combined with the short-term features to create a feature pool known as long short-term features, which is then employed for classification. To evaluate the performance of the proposed method, various traditional machine learning algorithms with various models are applied to the PhysioNet/CinC Challenge 2016 dataset, which is a collection of diverse heart sound data. The experimental results demonstrate that the proposed feature extraction approach increases the accuracy of heart disease diagnosis by nearly 10%.

Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models

“…The performance results were analyzed using SVM, KNN, and a Deep Neural Network (DNN) for model learning and classification. M. Guven [8] divided the entire PCG signal into short time periods and merged the features extracted through high-order statistics, energy, frequency domain, and Mel Coefficients. Classification performance was evaluated using specific algorithms of the Decision Tree, Naive Bayes (NB), Fine Gaussian, KNN, and Ensemble Method models.…”

“…PCG signals, which are biological signals, can be used to detect and classify heart diseases and abnormalities using machine and deep learning methods [7]. PCG sounds can be classified using machine learning classifiers such as the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) [8,9]. An SVM is an algorithm that classifies classes by determining the optimal decision boundary for class classification.…”

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

“…On the other hand, the artificial intelligence-based methods extract many time-domain, frequency-domain, or time-frequency-domain features from each PCG segment, which are then used to train a classification model. Several machine learning or deep learning algorithms, such as k-nearest neighbor, support vector machine, hidden Markov model, decision tree, k-means clustering, logistic regression, and neural networks, have been implemented to discriminate between S1, S2, S3, and S4 heart sounds, and also to distinguish heart sounds from murmurs [ 25 , 27 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ] and to recognize abnormal heart sounds [ 44 , 45 ]. Although they achieve high classification performance, artificial intelligence-based methods are far more complex than envelogram-based algorithms in terms of computational burden and require the a priori knowledge of the heart sounds for labelling PCG segments and training a classifier.…”