Pham Lam, McLoughlin Ian, Phan Huy, Tran Minh, Nguyen Truc, Palaniappan Ramaswamy
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:164-167. doi: 10.1109/EMBC44109.2020.9175704.
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods.
本文提出了一个强大的深度学习框架，用于从呼吸声记录中检测呼吸系统疾病。完整的检测过程首先涉及前端特征提取，即将记录转换为传达频谱和时间信息的频谱图。然后，后端深度学习模型将这些特征分类为呼吸系统疾病或异常类别。在ICBHI呼吸声基准数据集上进行的实验评估了该框架对声音进行分类的能力。本文有两个主要贡献。首先，我们对呼吸周期长度、时间分辨率和网络架构等因素如何影响最终预测准确性进行了广泛分析。其次，提出了一种基于深度学习的新型框架用于检测呼吸系统疾病，并且与现有方法相比表现出极其出色的性能。
