SRMAC -- Smoothed Recursive Moving Average Crossover for Real-Time Systolic Peak Detection in Photoplethysmography

TL;DR

This paper introduces the Smoothed Recursive Moving Average Crossover method for real-time PPG peak detection, enhancing accuracy and efficiency over previous methods, and provides a new open dataset for further research.

Contribution

It proposes a novel peak detection algorithm that reduces computational complexity and improves performance, along with releasing a new PPG dataset for benchmarking.

Findings

The proposed model outperforms previous crossover-based methods in precision and recall.

Achieves an average precision of 0.9937 and recall of 0.9968.

Demonstrates suitability for embedded device implementation.

Abstract

Purpose. Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Previous research has established the feasibility of PPG peak detection based on the crossover of moving averages. This paper proposes the Smoothed Recuarsive Moving Average Crossover, which eliminates the need for post-processing and nonlinear pre-processing of previous crossover-based peak detectors. The proposed model is advantageous regarding memory and computational complexity, making it attractive for implementations on embedded devices. Methods. Along with this paper, we make available a novel dataset comprising 66 minutes of PPG recordings. The optimization and assessment of the proposed peak detection model use this dataset. Its optimization is accomplished with the simple random search heuristic, while the leave-subject-out cross-validation method provides the means to assess its performance. The source code for all experiments reported in this research is also available in an online repository. Results. The experimental study examines the performance of the proposed model considering different arrangements of the PPG data. The experiments show that the proposed model performs better than the previous crossover-based approach from the literature regarding the precision and recall metrics. More specifically, our model has an average precision of 0.9937 and an average recall of 0.9968. Conclusion. The contribution of this research to the scientific community and literature is twofold. The dataset we collected is open for any researcher, and we improve upon the leading edge on crossover-based PPG peak detection. This improvement comes in terms of performance metrics and computational cost.