Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/146266
Author(s): | Ali Harimi Yahya Majd Abdorreza Alavi Gharahbagh Vahid Hajihashemi Zeynab Esmaileyan José J. M. Machado João Manuel R. S. Tavares |
Title: | Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
Issue Date: | 2022-12 |
Abstract: | 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%. |
Subject: | Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
Scientific areas: | Ciências médicas e da saúde Medical and Health sciences |
URI: | https://hdl.handle.net/10216/146266 |
Document Type: | Artigo em Revista Científica Internacional |
Rights: | openAccess |
Appears in Collections: | FEUP - Artigo em Revista Científica Internacional |
Files in This Item:
File | Description | Size | Format | |
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595311.1.png | 1st Page | 368.37 kB | image/png | View/Open |
595311.pdf | Paper | 2.3 MB | Adobe PDF | View/Open |
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