NDLI: A New multi-window detection approach for P-wave boundary points in electrocardiograms based on bilateral accumulative area

Content Provider	IEEE Xplore Digital Library
Author	Riqing Chen Jian Wu Yingsong Huang
Copyright Year	2015
Description	Author affiliation: Grad. Sch. at Shenzhen, Tsinghua Univ., Shenzhen, China (Riqing Chen; Jian Wu; Yingsong Huang)
Abstract	This study presented an efficient and robust multi-window detection method for P-wave boundary points in electrocardiograms on the basis of bilateral accumulative area. Through mathematical analysis, the local extreme points of bilateral accumulative area curves were respectively found in a fitting parabola, which might be regarded as a significant indicator for the morphological characteristic of boundary points. And, bilateral accumulative area curves of different window lengths had different sensitivities to the details and contours of signals. With combination of the multi-window and 12-lead synchronous detection, the proposed method could screen the optimal boundary points from all extreme points of different window lengths, and adaptively match the P-wave location. The results of the proposed method were evaluated on the dataset-3 of the standard CSE database. As a result, the value of sensitivity Se = 97.8% was obtained for the detection of P-wave, with the standard deviations of 3.9 ms and 4.8 ms respectively for the onset and offset of P-wave.
Starting Page	382
Ending Page	385
File Size	749962
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318379
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Standards Sensitivity Robustness Databases Algorithm design and analysis Threshold voltage
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Health Informatics Signal Processing Biomedical Engineering

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