Abstract：
Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more useful than conventional NNs by allowing accurate classification without requiring preprocessing and feature extraction. Utilizing the long short-term memory (LSTM) layers to reveal the sequence-based properties of the LS time series, a novel architecture consisting of a cascade of convolutional long short-term memory (ConvLSTM) and LSTM layers, namely ConvLSNet is developed, which permits highly accurate diagnosis of pulmonary disease states. By modeling the multichannel lung sounds through the ConvLSTM layer, the proposed ConvLSNet architecture can concurrently deal with the spatial and temporal properties of the six-channel LS recordings without heavy preprocessing or data transformation. Notably, the proposed model achieves a classification accuracy of 97.4 % based on LS data corresponding to three pulmonary conditions, namely asthma, COPD, and the healthy state. Compared with architectures consisting exclusively of CNN or LSTM layers, as well as those employing a cascade integration of 2DCNN and LSTM layers, the proposed ConvLSNet architecture exhibited the highest classification accuracy, while imposing the lowest computational cost as quantified by the number of parameters, training time, and learning rate.
摘要：
肺音（LS）的表征对于诊断呼吸道病理学是必不可少的。尽管传统的神经网络(NN)已被广泛用于肺音的自动诊断,通过允许准确的分类而不需要预处理和特征提取，深度神经网络可能比传统神经网络更有用。利用长短期记忆（LSTM）层揭示LS时间序列的基于序列的属性，一种由卷积长短期记忆(ConvLSTM)和LSTM层级联组成的新颖架构，即ConvLSNet的开发，这允许肺部疾病状态的高度准确的诊断。通过ConvLSTM层对多通道肺音进行建模，所提出的ConvLSNet架构可以同时处理六通道LS记录的空间和时间属性，而无需进行大量的预处理或数据转换。值得注意的是,所提出的模型基于对应于三种肺部状况的LS数据实现了97.4%的分类准确率，即哮喘,COPD,和健康的状态。与仅由CNN或LSTM层组成的架构相比，以及采用2DCNN和LSTM层级联集成的那些，提出的ConvLSNet架构表现出最高的分类精度，在施加由参数数量量化的最低计算成本的同时，培训时间,和学习率。
