CNN-based classification of phonocardiograms using fractal techniques

Deep Learning based heart sound classification is of significant interest in reducing the burden of manual auscultation through the automated detection of signals, including abnormal heartbeats. This work presents a method for classifying phonocardiogram (PCG) signals as normal or abnormal by applyi...

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Bibliographic Details
Published in:Biomedical signal processing and control 2023-09, Vol.86, p.105186, Article 105186
Main Authors: Riccio, Daniel, Brancati, Nadia, Sannino, Giovanna, Verde, Laura, Frucci, Maria
Format: Article
Language:English
Subjects:
CNN
Fractals
Phonocardiogram analysis
Signal classification
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Summary:Deep Learning based heart sound classification is of significant interest in reducing the burden of manual auscultation through the automated detection of signals, including abnormal heartbeats. This work presents a method for classifying phonocardiogram (PCG) signals as normal or abnormal by applying a deep Convolutional Neural Network (CNN) after transforming the signals into 2D color images. In particular, a new methodology based on fractal theory, which exploits Partitioned Iterated Function Systems (PIFS) to generate 2D color images from 1D signals is presented. PIFS have been extensively investigated in the context of image coding and indexing on account of their ability to interpolate and identify self-similar features in an image. Our classification approach has shown a high potential in terms of noise robustness and does not require any pre-processing steps or an initial segmentation of the signal, as instead happens in most of the approaches proposed in the literature. In this preliminary work, we have carried out several experiments on the database released for the 2016 Physionet Challenge, both in terms of different classification networks and different inputs to the networks, thus also evaluating the data quality. Among all experiments, we have obtained the best result of 0.85 in terms of modified Accuracy (MAcc). [Display omitted] •PIFS encode self-similarities in a 1D signal and transcode them into a 2D image.•The transcoding is noise-resistant and enables segmentation-free analysis of PCG.•The 2D image is a very compact representation of the signal that can feed a CNN.
ISSN:1746-8094
1746-8108
DOI:10.1016/j.bspc.2023.105186