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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Enhancing Convolutional Neural Networks for Cardiovascular Disease Detection: A Comparative Analysis of Data Augmentation Strategies Using Heart Sound Signals |
| Authors Name: | Dr.shalbha Chaudhary , Ms.Navita Bansal |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219124
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| Published Paper Id: | IJNRD2404851 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Effectively overseeing and swiftly identifying cardiovascular diseases (CVDs) are crucial for decreasing associated death rates. Identifying cardiovascular diseases (CVDs) can pose difficulties, especially when symptoms are not present. This has led to a rise in research dedicated to developing automated systems that can detect CVDs at an early stage. Lately, there has been a notable enthusiasm in utilising convolutional neural networks (CNNs) that have been trained on heart sound information, particularly the phonocardiogram (PCG). Convolutional neural networks (CNNs) generally require a large amount of annotated training data to achieve maximum performance. However, there is a scarcity of annotated datasets for phonocardiogram (PCG) that can differentiate between normal and abnormal cases. In order to address this difficulty, it is crucial to improve the classification performance of Convolutional Neural Networks (CNNs), which will allow for training on smaller PCG databases. This paper investigates two data augmentation (DA) techniques: window slicing with spectrogram, which entails dividing a single PCG into numerous signals turned into spectrogram data; and a synthetic spectrogram-based generative adversarial network, which generates synthetic data. The effectiveness of these data augmentation approaches is proven through studies on heart sound detection, accompanied by a comprehensive analysis of the results, including measures of accuracy, sensitivity, and specificity. |
| Keywords: | Cardiovascular diseases (CVDs,) Convolutional Neural Networks (CNNs), Phonocardiogram (PCG), Data Augmentation, Heart sound detection |
| Cite Article: | "Enhancing Convolutional Neural Networks for Cardiovascular Disease Detection: A Comparative Analysis of Data Augmentation Strategies Using Heart Sound Signals", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i405-i418, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404851.pdf |
| Downloads: | 00030 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404851 Registration ID: 219124 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i405-i418 Country: ghaziabad, Uttar pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404851 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404851 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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