TY - GEN
T1 - Extracting heartbeat intervals using self-adaptive method based on ballistocardiography(BCG)
AU - Ni, Hongbo
AU - He, Mingjie
AU - Xu, Guoxing
AU - Song, Yalong
AU - Zhou, Xingshe
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Ballistocardiogram (BCG) could reflect mechanical activity of cardiovascular system instead of ECG. And it is often acquired by sensitive mattress or chair without any constraints and limitations, but it contains many noise because of the impact of body and acquired equipment, those questions make heart rate detection difficult from the original BCG. In the paper, we propose an adaptive method which is used to extract heartbeat intervals (RR), and the method acquire automatically input parameters of Ensemble Empirical Mode Decomposition (EEMD) algorithm, and then decompose BCG signal using EEMD algorithm, and select adaptively decomposition component of BCG signal, whose periodicity is in accordance with the cardiac cycle completely as the target signal. Furthermore we detect the peak points and calculate the heartbeat intervals series using the target signal. In the result, the proposed method is tested using the BCG datasets from 18 subjects, including 8 females and 10 males (age 20–72). Finally, the heart rate from BCG will be compared with ECG, and the results are satisfactory and have a high accuracy.
AB - Ballistocardiogram (BCG) could reflect mechanical activity of cardiovascular system instead of ECG. And it is often acquired by sensitive mattress or chair without any constraints and limitations, but it contains many noise because of the impact of body and acquired equipment, those questions make heart rate detection difficult from the original BCG. In the paper, we propose an adaptive method which is used to extract heartbeat intervals (RR), and the method acquire automatically input parameters of Ensemble Empirical Mode Decomposition (EEMD) algorithm, and then decompose BCG signal using EEMD algorithm, and select adaptively decomposition component of BCG signal, whose periodicity is in accordance with the cardiac cycle completely as the target signal. Furthermore we detect the peak points and calculate the heartbeat intervals series using the target signal. In the result, the proposed method is tested using the BCG datasets from 18 subjects, including 8 females and 10 males (age 20–72). Finally, the heart rate from BCG will be compared with ECG, and the results are satisfactory and have a high accuracy.
KW - Ballistocardiogram
KW - Ensemble empirical mode decomposition
KW - Heartbeat intervals
UR - http://www.scopus.com/inward/record.url?scp=85028726304&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66188-9_4
DO - 10.1007/978-3-319-66188-9_4
M3 - 会议稿件
AN - SCOPUS:85028726304
SN - 9783319661872
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 37
EP - 47
BT - Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings
A2 - Abdulrazak, Bessam
A2 - Aloulou, Hamdi
A2 - Mokhtari, Mounir
PB - Springer Verlag
T2 - 15th International Conference on Smart Homes and Health Telematics, ICOST 2017
Y2 - 29 August 2017 through 31 August 2017
ER -