Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. Thi...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-12, Vol.22 (24), p.9569
Main Authors: Harimi, Ali, Majd, Yahya, Gharahbagh, Abdorreza Alavi, Hajihashemi, Vahid, Esmaileyan, Zeynab, Machado, José J M, Tavares, João Manuel R S
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Auscultation
biomedical signal
Cardiovascular disease
Classification
Datasets
deep learning
Digital signal processors
Electrocardiography
Heart
Heart Diseases
Heart Sounds
Humans
Machine Learning
Neural networks
Neural Networks, Computer
Noise
Performance evaluation
phonocardiogram
Quality assessment
Signal processing
Signal Processing, Computer-Assisted
signal to image transform
Sound
Wavelet transforms
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Summary:Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22249569