TY - GEN
T1 - The automatic detection and analysis of electrocardiogram based on Lorenz Plot
AU - Wang, Wenqi
AU - Wei, Yangjie
AU - Guan, Nan
AU - Wang, Yi
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2015
Y1 - 2015
N2 - Lorenz Plot is widely used in analysis of heart rate variability because of its high sensitivity and high individuation. However, due to the high computational complexity and excessive reliance on experience based tuning, the effectiveness and accuracy of the traditional analysis methods based on Lorenz Plot are both low. In this paper, an automatic detection method according to the shape of Lorenz Plot is proposed, and a sequence of error analysis has been conducted according to the experimental results. First, the redundant points are automatically eliminated through coordinate transformation and their distribution characteristics along the X-axis and Y-axis. Then, we divide the coverage area of scattering points into several parts, and determine the shape of Lorenz Plot according to regional characteristics such as area scale, symmetry and slope. Finally, we conduct experiments with data from the MIT-BIH database, which prove that this method could quickly and accurately pick out the normal shapes from the complex shapes of Lorenz Plots. The detection accuracy of our method is above 90%, and the time complexity is O(n).
AB - Lorenz Plot is widely used in analysis of heart rate variability because of its high sensitivity and high individuation. However, due to the high computational complexity and excessive reliance on experience based tuning, the effectiveness and accuracy of the traditional analysis methods based on Lorenz Plot are both low. In this paper, an automatic detection method according to the shape of Lorenz Plot is proposed, and a sequence of error analysis has been conducted according to the experimental results. First, the redundant points are automatically eliminated through coordinate transformation and their distribution characteristics along the X-axis and Y-axis. Then, we divide the coverage area of scattering points into several parts, and determine the shape of Lorenz Plot according to regional characteristics such as area scale, symmetry and slope. Finally, we conduct experiments with data from the MIT-BIH database, which prove that this method could quickly and accurately pick out the normal shapes from the complex shapes of Lorenz Plots. The detection accuracy of our method is above 90%, and the time complexity is O(n).
UR - https://www.scopus.com/pages/publications/84964556039
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84964556039&origin=recordpage
U2 - 10.1109/ROBIO.2015.7418841
DO - 10.1109/ROBIO.2015.7418841
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467396745
T3 - 2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
SP - 644
EP - 649
BT - 2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
PB - IEEE
T2 - IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
Y2 - 6 December 2015 through 9 December 2015
ER -