论文标题
深度学习框架用于预测呼吸声异常
Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds
论文作者
论文摘要
本文提出了一个可靠的深度学习框架，用于对呼吸周期的异常进行分类。最初，我们的框架从前端特征提取步骤开始。此步骤旨在将呼吸输入声音转换为二维光谱图，其中光谱和时间特征都得到很好的形式。接下来，将C-DNN和自动编码器网络的合奏应用于四类呼吸异常循环中。在这项工作中，我们对2017年生物医学健康信息学内部会议（ICBHI）基准数据集进行了实验。结果，我们以0.49的平均得分为0.42，实现竞争性能。
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.
