Orphanidou Christina
Department of Electrical and Computer Engineering, University of Cyprus, University House "Anastasios G. Leventis", Panepistimiou Avenue, 2109 Aglanzia, P.O.Box. 20537, 1678 Nicosia, Cyprus; Kios Centre for Intelligent Systems and Networks, University of Cyprus, P.O.Box 20537, 1678 Nicosia, Cyprus; Oxygen Research Ltd, Vassileos Constantinou 8, Limassol 3075, Cyprus.
Comput Biol Med. 2017 Feb 1;81:45-54. doi: 10.1016/j.compbiomed.2016.12.005. Epub 2016 Dec 7.
A new method for extracting the respiratory rate from ECG and PPG obtained via wearable sensors is presented. The proposed technique employs Ensemble Empirical Mode Decomposition in order to identify the respiration "mode" from the noise-corrupted Heart Rate Variability/Pulse Rate Variability and Amplitude Modulation signals extracted from ECG and PPG signals. The technique was validated with respect to a Respiratory Impedance Pneumography (RIP) signal using the mean absolute and the average relative errors for a group ambulatory hospital patients. We compared approaches using single respiration-induced modulations on the ECG and PPG signals with approaches fusing the different modulations. Additionally, we investigated whether the presence of both the simultaneously recorded ECG and PPG signals provided a benefit in the overall system performance. Our method outperformed state-of-the-art ECG- and PPG-based algorithms and gave the best results over the whole database with a mean error of 1.8bpm for 1min estimates when using the fused ECG modulations, which was a relative error of 10.3%. No statistically significant differences were found when comparing the ECG-, PPG- and ECG/PPG-based approaches, indicating that the PPG can be used as a valid alternative to the ECG for applications using wearable sensors. While the presence of both the ECG and PPG signals did not provide an improvement in the estimation error, it increased the proportion of windows for which an estimate was obtained by at least 9%, indicating that the use of two simultaneously recorded signals might be desirable in high-acuity cases where an RR estimate is required more frequently.
提出了一种从通过可穿戴传感器获得的心电图（ECG）和光电容积脉搏波描记图（PPG）中提取呼吸率的新方法。所提出的技术采用总体经验模态分解，以便从从ECG和PPG信号中提取的受噪声干扰的心率变异性/脉率变异性和调幅信号中识别呼吸“模式”。使用一组门诊医院患者的平均绝对误差和平均相对误差，针对呼吸阻抗肺描记图（RIP）信号对该技术进行了验证。我们将使用ECG和PPG信号上的单一呼吸诱导调制的方法与融合不同调制的方法进行了比较。此外，我们研究了同时记录的ECG和PPG信号的存在是否对整体系统性能有帮助。我们的方法优于基于ECG和PPG的现有算法，并且在整个数据库上给出了最佳结果，使用融合的ECG调制进行1分钟估计时平均误差为1.8bpm，相对误差为10.3%。在比较基于ECG、PPG和ECG/PPG的方法时未发现统计学上的显著差异，这表明在使用可穿戴传感器的应用中，PPG可以用作ECG的有效替代方案。虽然ECG和PPG信号的同时存在并未改善估计误差，但它将获得估计的窗口比例至少提高了9%，这表明在需要更频繁进行RR估计的高 acuity 病例中，使用两个同时记录的信号可能是可取的。
