Respiratory diseases are among the most common causes of severe illness and death worldwide. Prevention and early diagnosis are essential to limit or even reverse the trend that characterizes the diffusion of such diseases. In this regard, the development of advanced computational tools for the analysis of respiratory auscultation sounds can become a game changer for detecting disease-related anomalies, or diseases themselves. In this work, we propose a novel learning framework for respiratory auscultation sound data. Our approach combines state-of-the-art feature extraction techniques and advanced deep-neural-network architectures. Remarkably, to the best of our knowledge, we are the first to model a recurrent-neural-network based learning framework to support the clinician in detecting respiratory diseases, at either level of abnormal sounds or pathology classes. Results obtained on the ICBHI benchmark dataset show that our approach outperforms competing methods on both anomaly-driven and pathology-driven prediction tasks, thus advancing the state-of-the-art in respiratory disease analysis.
The boosted decision tree model proposed in @cite_13 utilizes two different types of features: MFCCs and low-level features extracted with the help of the library @cite_1 . This method was mainly evaluated on a binary prediction setting (i.e., healthy or unhealthy), achieving accuracy up to 85
@cite_1 @cite_13
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Comunicacio presentada a la 14th International Society for Music Information Retrieval Conference, celebrada a Curitiba (Brasil) els dies 4 a 8 de novembre de 2013. In modern medicine, every cardiac assessment or respiratory check-up includes an audio auscultation during which one the medical specialist listens to sounds from the patient body with different tools (stethoscope, sonography). This shows how important sound analysis is for heart and lungs disease detection. During the IeBRI 2017 challenge, a database of 920 records acquired from 126 subject, was used to find a method that predicted if a respiratory cycle contains, or not, adventitious sounds like crackles, wheezes or both of them. The team which submits the best results reached around 50 of correct detection. Using a machine learning approach with a boosted decisional tree model and more audio features leads to the same results. A new approach consists in creating a new model at the patient level, which is able to decide if a patient sounds sick or not, by taking as input the predicted results of the first classification model. This new model permits to reach 85 of good predictions and could be used as a tool for helping doctors to make better diagnosis.