Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds

• URL: http://arxiv.org/abs/2012.13668v1
• Date: Sat, 26 Dec 2020 03:09:36 GMT
• Title: Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds
• Authors: Dat Ngo, Lam Pham, Anh Nguyen, Ben Phan, Khoa Tran, Truong Nguyen
• Abstract summary: This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset.
• Score: 11.375037967010224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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