Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds
This work addresses the problem of classifying respiratory sound anomalies for medical diagnosis, providing competitive performance on a standard benchmark.
This paper developed a deep learning framework to classify respiratory sound anomalies into four categories. The framework achieved an ICBHI average score of 0.49 and a harmonic score of 0.42 on the 2017 ICBHI benchmark dataset.
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.