Feature-Based Fusion Using CNN for Lung and Heart Sound Classification.
Feature-Based Fusion Using CNN for Lung and Heart Sound Classification.
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DOI：
10.3390/s22041521
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发表时间：
2022-02-16
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影响因子：
--
通讯作者：
Lee Y
中科院分区：
文献类型：
--
作者：
Tariq Z;Shah SK;Lee Y
由于音频数据的复杂性、其动态的时间域和频域特性，肺音或心音的分类是具有挑战性的。在数据量小或数据不平衡和高噪声的情况下，也很难检测到心肺疾病。此外，数据质量是提高深度学习性能的一个相当大的陷阱。在本文中，我们提出了一种新的基于特征的融合网络FDC-FS用于心音和肺音的分类。FDC-FS框架旨在有效地迁移从音频数据集构建的三个不同深度神经网络模型的学习。所提出的转移学习的创新之处在于将音频数据转换为图像向量，并从三个特定模型转换为一个更适合深度学习的融合模型。我们使用了两个公开可用的数据集进行这项研究，即来自ICHBI 2017挑战的肺音数据和心脏挑战数据。我们应用了数据增强技术，如噪声失真、音调漂移和时间拉伸，处理了这些数据集中的一些数据问题。重要的是，我们从音频样本中提取了三个独特的特征，即语谱图、MFCC和Chromagram。最后，通过对音频特征转换后的图像特征向量的反馈，构建了三种最优卷积神经网络模型的融合。我们证实了所提出的融合模型与最先进的作品相比的优越性。FDC-FS对基于频谱图的肺音分类的最高准确率为99.1%，而对基于频谱图和色谱图的心音分类的准确率为97%。
Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains. It is also very difficult to detect lung or heart conditions with small amounts of data or unbalanced and high noise in data. Furthermore, the quality of data is a considerable pitfall for improving the performance of deep learning. In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds. The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets. The innovation of the proposed transfer learning relies on the transformation from audio data to image vectors and from three specific models to one fused model that would be more suitable for deep learning. We used two publicly available datasets for this study, i.e., lung sound data from ICHBI 2017 challenge and heart challenge data. We applied data augmentation techniques, such as noise distortion, pitch shift, and time stretching, dealing with some data issues in these datasets. Importantly, we extracted three unique features from the audio samples, i.e., Spectrogram, MFCC, and Chromagram. Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features. We confirmed the superiority of the proposed fusion model compared to the state-of-the-art works. The highest accuracy we achieved with FDC-FS is 99.1% with Spectrogram-based lung sound classification while 97% for Spectrogram and Chromagram based heart sound classification.
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