Joint Optimization of Recurrence Plot Encoding and CNN Model Based on Heuristic Algorithms

Peripheral waveform analysis (PWA), which is generally used to reveal hidden health status information from peripheral pulse signals, typically involves three procedures: signal preprocessing, feature extraction, and pattern classification. With the advancement of data-driven deep neural network methodologies, feature extraction and pattern classification have progressively converged into end-to-end neural networks, where the final layer of the network is equivalent to conventional pattern classifiers. However, the performance of deep learning models heavily relies on the quality of the dataset, rendering data signal preprocessing a crucial component. This study proposes a framework that integrates signal preprocessing, feature extraction, and pattern classification into a unified learning approach using heuristic algorithms, enabling the automatic discovery of optimal data encoding methods and their corresponding models. Initially, the search space is defined based on parameters relevant to signal preprocessing, and a fitness function is constructed utilizing CNN. Subsequently, the optimal combination of data preprocessing and CNN is determined through the heuristic algorithm Particle Swarm Optimization (PSO). The proposed method was evaluated in the dataset comprising authentic clinical cases of type 2 diabetes screening involving approximately 200 volunteers. The model derived from this framework demonstrates the capability to effectively discriminate between healthy volunteers and those with diabetes, achieving the highest accuracy of 93.6%. Compared to state-of-the-art algorithms, the proposed model was shown to be competitive in both accuracy and time cost.