Deep learning framework applied for predicting anomaly of respiratory sounds
2021 International Symposium on Electrical and Electronics …, 2021•ieeexplore.ieee.org
This paper proposes a robust deep learning framework used for classifying anomaly of
respiratory cycles. Initially, our framework starts with front-end feature extraction step. This
step aims to transform the respiratory input sound into a two-dimensional spectrogram
where both spectral and temporal features are well presented. Next, an ensemble of C-DNN
and Autoencoder networks is then applied to classify into four categories of respiratory
anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on …
respiratory cycles. Initially, our framework starts with front-end feature extraction step. This
step aims to transform the respiratory input sound into a two-dimensional spectrogram
where both spectral and temporal features are well presented. Next, an ensemble of C-DNN
and Autoencoder networks is then applied to classify into four categories of respiratory
anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on …
This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C-DNN and Autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a result, we achieve competitive performances with ICBHI average score of 0.49, ICBHI harmonic score of 0.42.
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