Why the study?
Heart disease mortality can be mitigated if cardiovascular disorders are detected timely and efficiently using simple methods such as auscultation and phonocardiograms.
Does the deployment of customized transfer learning models on the harmonic spectrogram of phonocardiograms accurately detect pathological cardiac sounds?
Does the deployment of customized transfer learning models on the harmonic spectrogram of phonocardiograms accurately detect pathological cardiac sounds?
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Applying transfer learning models to the harmonic spectrogram sub-component of phonocardiograms provides high accuracy in detecting pathological cardiac sounds.
There has been a steady rise in the number of deaths throughout the world due to heart diseases. This can be mitigated, to a large extent, if cardiovascular disorders can be detected timely and efficiently. Electrocardiograms (ECGs) and phonocardiograms (PCGs) are the two most popular diagnostic tools used for detecting cardiac problems. Another simple and efficient method for quickly identifying cardiovascular illness is Auscultation. In this work, the cardiac sound signal has been transformed into its equivalent spectrogram representation for detecting cardiac problems. The novelty of the proposed approach is the deployment of customized transfer learning (TL) models on sub-component of a spectrogram called Harmonic Spectrogram, instead of taking full spectrogram. Experiments have been conducted using PhysioNet 2016, which is considered a benchmark dataset. TL models, viz. MobileNet, DenseNet121, InceptionResnetV2, VGG16, and InceptionV3 have been put to use for categorizing cardiac sound waves as normal or pathological. The results exhibit that the MobileNet has achieved greater accuracy (93.45%), recall (92.46%), Precision (97.82%), F1 Score (95.06%) than many of the peers.
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Arora et al. (2023) studied this question.
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