Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification
This work addresses respiratory anomaly detection for healthcare applications, but it is incremental as it applies existing optimization and transfer learning techniques to a specific domain.
The study tackled the classification of respiration patterns from non-contact sensing data by optimizing a 1D-CNN architecture using a genetic algorithm, achieving improved efficiency through transfer learning to reduce computational time.
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.