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PCGmix: A Data-Augmentation Method for Heart-Sound Classification
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DOI：10.1109/JBHI.2024.3458430.png)
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Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, responsible for 32% of all deaths, with the annual death toll projected to reach 23.3 million by 2030. The early identification of individuals at high risk of CVD is crucial for the effectiveness of preventive strategies. In the field of deep learning, automated CVD-detection methods have gained traction, with phonocardiogram (PCG) data emerging as a valuable resource. However, deep-learning models rely on large datasets, which are often challenging to obtain. In recent years, data augmentation has become a viable solution to the problem of scarce data. In this paper, we propose a novel data-augmentation technique named PCGmix, specifically engineered for the augmentation of PCG data. The PCGmix algorithm employs a process of segmenting and reassembling PCG recordings, incorporating meticulous interpolation to ensure the preservation of the cardinal diagnostic features pertinent to CVD detection. The empirical assessment of the PCGmix method was utilized on a publicly available database of normal and abnormal heart-sound recordings. To evaluate the impact of data augmentation across a range of dataset sizes, we conducted experiments encompassing both limited and extensive amounts of training data. The experimental results demonstrate that the novel method is superior to the compared state-of-the-art, time-series augmentation. Notably, on limited data, our method achieves comparable accuracy to the no-augmentation approach when trained on 31% to 69% larger datasets. This study suggests that PCGmix can enhance the accuracy of deep-learning models for CVD detection, especially in data-constrained environments.
Keyword:
Heart
Feature extraction
Phonocardiography
Spectrogram
Data models
Heart beat
Data augmentation
phonocardiogram
heart sounds
abnormal heart-sound detection
deep learning
neural networks
machine learning
期刊
IF:
6.8
论文数:
4.7K
被引数:
2.0W
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