A Voting Approach for Heart Sounds Classification Using Discrete Wavelet Transform and CNN Architecture

Cardiovascular diseases (CVDs) are a group of diseases that affect the heart or blood vessels and are the leading cause of mortality around the world. The main focus of this work is to classify heart sounds accurately before the condition of the heart worsens. Over the past few decades, audio-based...

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Bibliographic Details
Published in:SN computer science 2024-02, Vol.5 (2), p.251, Article 251
Main Authors: Das, Sunanda, Ahsan, Sk. Md. Masudul, Rahman, Mahfujur, Karim, Md. Shahriar
Format: Article
Language:English
Subjects:
Approximation
Audio data
Blood vessels
Cardiovascular disease
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Decomposition
Deep learning
Discrete Wavelet Transform
Employment
Feature extraction
Fourier transforms
Heart
Information Systems and Communication Service
Lagrange multiplier
Machine learning
Neural networks
Original Research
Pattern Recognition and Graphics
Software Engineering/Programming and Operating Systems
Sound
Support vector machines
Vision
Wavelet transforms
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Summary:Cardiovascular diseases (CVDs) are a group of diseases that affect the heart or blood vessels and are the leading cause of mortality around the world. The main focus of this work is to classify heart sounds accurately before the condition of the heart worsens. Over the past few decades, audio-based classifications of medical data have received high consideration among various researchers. However, there are mainly two drawbacks while working with audio data. (1) variable length of the audio data and, (2) an inadequate number of class quintessential audio samples. In this work, reflection operation and the sliding window approach are employed to generate fixed-sized audio data. The proposed method applied Discrete Wavelet Transform (DWT) and Mel-Spectrogram for feature extraction. Furthermore, different augmentation techniques such as time-stretching and pitch-shifting are utilized in the method so that the proposed deep learning-based CNN architecture can be trained on a large amount of data. The proposed method is verified using two datasets provided by the ‘PASCAL Heart Sounds Challenge’, each of which contains a small number of heart sound samples of various lengths. In comparison, the experimental outcomes exhibit that the proposed approach outperforms many state-of-the-art methods with respect to sensitivity, specificity, precision, Youden index, etc.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-02580-9