Short unsegmented PCG classification based on ensemble classifier

Abstract

Diseases associated with the heart are one of the main reasons of death worldwide. Hence, early examination of the heart is important. For analysis of cardiac disorders, a study of heart sounds is a crucial and beneficial approach.Still, automated classification of heart sounds is a challenging task that mainly depends on segmentation of heart sounds and derivation of features using segmented samples. In the literature available for PCG classification provided by PhysioNet/CinC Challenge 2016, most of the research has focused on enhancing the accuracy of the classification model based on complicated segmentation processes and has failed to improve the sensitivity. In this paper, we present an automated heart sound classification by eliminating the segmentation steps using multidomain features, which results in enhanced sensitivity. The study is based on homomorphic envelogram, mel frequency cepstral coefficient (MFCC), power spectral density (PSD), and multidomain feature extraction. The extracted features are trained using the 5-fold cross-validation method based on an ensemble boosting algorithm over 100 independent iterations. Our proposed design is evaluated using public datasets published in PhysioNet/Computers in Cardiology Challenge 2016. Accuracy of 92.47% with improved sensitivity of 94.08% and specificity of 91.95% is achieved using our model. The output performance proves that our proposed model offers superior performance results.

Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals

“…Singh et al [27] initially applied KNN on unsegmented heart sounds recording and got 90.00% accuracy. Later, Singh et al [29] improved the accuracy to 92.47% by applying a set of classifiers. Whitaker et al [21], Tang et al [24], and Nogueira et al [28] employed SVM with different structures to build their models and achieved 89.26%, 88.00%, and 87.85% accuracy, respectively.…”

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

“…Though the ML models have been relatively efficient and popular in recent decades, training methods and the amount of feeding data have contributed to their success. More often researchers used 70:30 (training:validation), 80:20, or 90:10 partition to simulate the models [ 11 , 13 , 27 , 42 , 43 ]. The data partition scale for training and testing to be given during the simulation is assumed to be still unexplained and without any principled reason-based calculation.…”

Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application

“…The study in [ 19 ] used the Physionet dataset to perform anomaly detection using signal-to-noise ratio (SNR) and 1D Convolutional Neural Networks. In [ 20 ], the researchers presented a heart sound classification technique using multidomain features instead of heartbeat segmentation. They achieved an accuracy of 92.47% with improved sensitivity of 94.08% and specificity of 91.95%.…”

“…They achieved an accuracy of 92.47% with improved sensitivity of 94.08% and specificity of 91.95%. The researchers in [ 20 ] used a Butterworth bandpass filter and a pretrained CNN model for CVD classification. In [ 21 ], the authors used deep neural network architectures and one-dimensional convolutional neural networks (1D-CNN) with a feed-forward neural network (F-NN) to classify normal and abnormal PCG signals from the Physionet dataset.…”