TY - GEN
T1 - Efficient Communication Overhead Reduction using Polygonal Approximation-based ECG Signal Compression
AU - Lee, Seungmin
AU - Jeong, Yoosoo
AU - Kwak, Junho
AU - Park, Daejin
AU - Park, Kil Houm
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/18
Y1 - 2019/3/18
N2 - ECG signal requires a high sampling frequency of 100 to 1000 Hz, as well as long measurement times of longer than 24 hours. Therefore, efficient data compression for storage and transmission of data is required. ECG signal can be represented by a fiducial point composed of the onset, offset, and peak, which are essential for ECG signal analysis. Detecting the onset and offset are ambiguous because the feature values are similar to those of the surrounding samples. In this paper, we represent ECG signal as vertices by polygonal approximation, and suggest an auxiliary signal generated by the amplitude change rate between vertices. The proposed method can compress the number of data bits to about 89.26% and preserve the fiducial points as vertices. Also, we analyze the features of each vertices and determine the fiducial points. The clustering results of QRS complex were stable with the QT-DB provided by Physionet.
AB - ECG signal requires a high sampling frequency of 100 to 1000 Hz, as well as long measurement times of longer than 24 hours. Therefore, efficient data compression for storage and transmission of data is required. ECG signal can be represented by a fiducial point composed of the onset, offset, and peak, which are essential for ECG signal analysis. Detecting the onset and offset are ambiguous because the feature values are similar to those of the surrounding samples. In this paper, we represent ECG signal as vertices by polygonal approximation, and suggest an auxiliary signal generated by the amplitude change rate between vertices. The proposed method can compress the number of data bits to about 89.26% and preserve the fiducial points as vertices. Also, we analyze the features of each vertices and determine the fiducial points. The clustering results of QRS complex were stable with the QT-DB provided by Physionet.
KW - Electrocardiogram
KW - Fiducial point
KW - Polygonal approximation
KW - QRS complex
UR - http://www.scopus.com/inward/record.url?scp=85063918234&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC.2019.8668974
DO - 10.1109/ICAIIC.2019.8668974
M3 - Conference contribution
AN - SCOPUS:85063918234
T3 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
SP - 58
EP - 61
BT - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Y2 - 11 February 2019 through 13 February 2019
ER -