Elgendi Mohamed, Norton Ian, Brearley Matt, Abbott Derek, Schuurmans Dale
Department of Computing Science, University of Alberta, 2-32 Athabasca Hall, T6G 2E1 Edmonton, Canada.
Biomed Eng Online. 2014 Sep 25;13:139. doi: 10.1186/1475-925X-13-139.
Analyzing acceleration photoplethysmogram (APG) signals measured after exercise is challenging. In this paper, a novel algorithm that can detect a waves and consequently b waves under these conditions is proposed. Accurate a and b wave detection is an important first step for the assessment of arterial stiffness and other cardiovascular parameters.
Nine algorithms based on fixed thresholding are compared, and a new algorithm is introduced to improve the detection rate using a testing set of heat stressed APG signals containing a total of 1,540 heart beats.
The new a detection algorithm demonstrates the highest overall detection accuracy--99.78% sensitivity, 100% positive predictivity--over signals that suffer from 1) non-stationary effects, 2) irregular heartbeats, and 3) low amplitude waves. In addition, the proposed b detection algorithm achieved an overall sensitivity of 99.78% and a positive predictivity of 99.95%.
The proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination.
分析运动后测量的加速度光电容积脉搏波（APG）信号具有挑战性。本文提出了一种在这些条件下能够检测a波并进而检测b波的新算法。准确检测a波和b波是评估动脉僵硬度及其他心血管参数的重要第一步。
比较了九种基于固定阈值的算法，并引入一种新算法，使用一组包含总共1540次心跳的热应激APG信号测试集来提高检测率。
新的a波检测算法在遭受1）非平稳效应、2）不规则心跳和3）低振幅波的信号上展现出最高的总体检测准确率——灵敏度为99.78%，阳性预测值为100%。此外，所提出的b波检测算法总体灵敏度达到99.78%，阳性预测值为99.95%。
所提出的算法通过避免在阈值确定中进行人工干预，在实时应用中具有优势。
