Automatic Identification of Heart Abnormalities Using PCG Signals - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2022

Automatic Identification of Heart Abnormalities Using PCG Signals

Narendran Srinivasan
  • Fonction : Auteur
  • PersonId : 1334365
Devi Vijayan
  • Fonction : Auteur
  • PersonId : 1334366

Résumé

A phonocardiogram (PCG) signal holds aural information generated by the heart during a cycle. A close examination of the PCG signal can reveal valuable cardiac information thereby allowing detection of abnormalities and diagnosis of heart diseases. An automation-aided analysis of PCG signals can play a vital role in the medical field, especially in remote patient monitoring, apart from being a very efficient approach. In this study, PCG signals are classified under 5 different classes based on the features extracted. The five classes are normal, mitral stenosis, mitral regurgitation, mitral valve prolapse, aortic stenosis (N, MS, MR, MVP, AS). Mel-Frequency Cepstral Coefficients (MFCCs) are extracted from the PCG audio signals and fed into a deep learning based convolutional neural network (CNN). The proposed approach achieves a maximum accuracy of 99.64% which outperforms the existing state-of-the-art approaches.
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mercredi 1 janvier 2025
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Dates et versions

hal-04388160 , version 1 (11-01-2024)

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V. Pravin, Narendran Srinivasan, Palukuri Rohith, U. Vishnu Arvind, Devi Vijayan. Automatic Identification of Heart Abnormalities Using PCG Signals. 6th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Feb 2022, Chennai, India. pp.314-324, ⟨10.1007/978-3-031-11633-9_22⟩. ⟨hal-04388160⟩
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