k-nearest neighbors algorithm

Pattern recognition (psychology)

Phonocardiogram

Support vector machine

Filter (signal processing)

Heartbeat

Spectral density

Mel-frequency cepstrum

Artificial intelligence

Hilbert–Huang transform

Cross-validation

Computer science

Feature extraction

Speech recognition

Hilbert transform

Computer vision

Computer security

Artificial neural network

Telecommunications

Feature vector

Cepstrum

Journal of King Saud University - Computer and Information Sciences

Heartbeat sounds are biological signals used in the early diagnosis of cardiovascular diseases. Digital heartbeat sound recordings, called phonocardiogram (PCG), are used in the determination and automatic classification of possible heart diseases. Healthy and pathological PCG signals are non-stationary signals and conventional feature extraction methods are insufficient in classifying these signals. In this study, PCG signals in healthy and four pathological categories are decomposed into intrinsic mode functions (IMFs) by Hilbert-Huang transform. Mel-frequency cepstral coefficient (MFCC) features were extracted from each mode to investigate the effect of the modes obtained by Hilbert-Huang transform on the classification of PCG signals. Genetic algorithm was used as feature selection method and k-nearest neighbor (KNN), multilayer perceptron (MLP), support vector machine (SVM) and deep neural network (DNN) machine learning methods were used as classifier. We have implemented multi classifications of five PCG classes (healthy, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse) by using 5-fold cross validation and 10 × 5-fold cross validation Data Analysis Protocol (DAP) framework. The results show that the DNN model provides the highest classification performance with 98.9% precision, 98.7% recall, 98.8% F1-score and 98.9% accuracy using 5-fold cross validation, and Matthews correlation coefficient of 0.981 using the DAP method.

Effect of Hilbert-Huang transform on classification of PCG signals using machine learning