Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise

TL;DR

This paper introduces BayeSlope, an adaptive, energy-efficient method for accurate R-peak detection in wearable ECG sensors during high-intensity exercise, outperforming existing algorithms in robustness and energy consumption.

Contribution

The paper presents BayeSlope, a novel unsupervised Bayesian method combined with an adaptive online system for robust ECG R-peak detection in challenging exercise conditions.

Findings

BayeSlope achieves 99.3% F1 score during intense cycling.

The online adaptive process reaches 99% F1 across various exercise intensities.

Energy consumption is reduced by up to 38.7% with the adaptive method.

Abstract

Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sudden physiological changes occur to the signals, compromising the robustness of standard algorithms. Methods: Our method, called BayeSlope, is based on unsupervised learning, Bayesian filtering, and non-linear normalization to enhance and correctly detect the R peaks according to their expected positions in the ECG. Furthermore, as BayeSlope is computationally heavy and can drain the device battery quickly, we propose an online design that adapts its robustness to sudden physiological changes, and its complexity to the heterogeneous resources of modern embedded platforms. This method combines BayeSlope with a lightweight algorithm, executed in cores with different capabilities, to reduce the energy consumption while preserving the accuracy. Results: BayeSlope achieves an F1 score of 99.3% in experiments during intense cycling exercise with 20 subjects. Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1.55+-0.54~mJ. Conclusion: We propose a highly accurate and robust method, and a complete energy-efficient implementation in a modern ultra-low-power embedded platform to improve R peak detection in challenging conditions, such as during high-intensity exercise. Significance: The experiments show that BayeSlope outperforms a state-of-the-art algorithm up to 8.4% in F1 score, while our online adaptive method can reach energy savings up to 38.7% on modern heterogeneous wearable platforms.