In this study, we present a deep learning-based approach for time-series
respiration data classification. The dataset contains regular breathing
patterns as well as various forms of abnormal breathing, obtained through
non-contact incoherent light-wave sensing (LWS) technology. Given the
one-dimensional (1D) nature of the data, we employed a 1D convolutional neural
network (1D-CNN) for classification purposes. Genetic algorithm was employed to
optimize the 1D-CNN architecture to maximize classification accuracy.
Addressing the computational complexity associated with training the 1D-CNN
across multiple generations, we implemented transfer learning from a
pre-trained model. This approach significantly reduced the computational time
required for training, thereby enhancing the efficiency of the optimization
process. This study contributes valuable insights into the potential
applications of deep learning methodologies for enhancing respiratory anomaly
detection through precise and efficient respiration classification.

本研究提出了一种基于深度学习的时间序列呼吸数据分类方法，采用了一维卷积神经网络 (1D-CNN) 对呼吸数据进行分类，通过遗传算法优化了 1D-CNN 的架构，同时对预训练模型进行了迁移学习以提高算法的训练效率。这项研究为运用深度学习方法提升呼吸异常检测的准确性和效率提供了有价值的见解。