Multiclass Classification of Systolic and Diastolic Peaks on Open Benchmark PPG Signals: Performance Evaluation

Abstract

Photoplethysmogram (PPG) signals contain valuable health information that is in the relation between the volumetric variations of blood circulation and the cardiovascular and respiratory systems. This study introduces the performance evaluation on open clinical benchmark PPG signals with a multiclass neural network with random weights (NNRW) classification method for systolic peak and diastolic point detection. The best performance of the peak and point detection is crucial to be achieved at the early stage for extracting further valuable information in addition to future predictions of cardiovascular-related illness. Various open clinical datasets of PPG signals have been introduced, however, there is a lack of information on peak annotations. Due to the lack of peak annotation information, it is time-consuming to be prepared. One suitable clinical benchmark dataset with peak annotation information for peak detection has been previously evaluated, however, it cannot be generalized and rely upon only one dataset. Therefore, for generalization, there is a new open clinical benchmark dataset that is found in the year 2018 and our own collected data from normal participants is utilized in this study. The findings exhibit more convincing overall accuracy and Gmean of testing results with 94.86 and 94.74 percent, respectively. The findings of the comparison with previous work indicate that the proposed methodology to predict PPG-based multi-class systolic and diastolic points is more generalizable.