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QRS Detection Based on Improved Adaptive Threshold
Joint Authors
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-15
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Cardiovascular disease is the first cause of death around the world.
In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG) analysis algorithm plays an important role, whose first step is QRS detection.
The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage.
In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems.
However, the detection rate of the threshold algorithm still calls for improvement.
An improved adaptive threshold algorithm for QRS detection is reported in this paper.
The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting.
The detection rate is 99.41%, the sensitivity (Se) is 99.72%, and the specificity (Sp) is 99.69% on the MIT-BIH Arrhythmia database.
A comparison is also made with two other algorithms, to prove our superiority.
The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.
American Psychological Association (APA)
Lu, Xuanyu& Pan, Maolin& Yu, Yang. 2018. QRS Detection Based on Improved Adaptive Threshold. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187387
Modern Language Association (MLA)
Lu, Xuanyu…[et al.]. QRS Detection Based on Improved Adaptive Threshold. Journal of Healthcare Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1187387
American Medical Association (AMA)
Lu, Xuanyu& Pan, Maolin& Yu, Yang. QRS Detection Based on Improved Adaptive Threshold. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187387
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187387