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Adaptive thresholding based low complexity QRS detection algorithm
Abstract
In this study, a QRS detection algorithm with a low processing load based on time-domain thresholding is proposed. The ECG signal is filtered only with a low pass filter to reduce the computational load. After the filtering, derivation and squaring are also performed. In the Thresholding stage, a linear decreasing threshold voltage method using addition operation instead of multiplication is proposed. Simulations on MIT-BIT Arrhythmia Database have yielded 99.2925% sensitivity (% Se) and 99.6759% positive predictivity (+ P). The proposed algorithm is compared with two similar algorithms in terms of both performance and processing load. It is shown that the proposed algorithm is better than its counterparts, especially in terms of processing load. However, it is observed that it gave worse results in terms of Sensitivity (% Se).
Keywords
References
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Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Authors
Publication Date
January 16, 2023
Submission Date
February 18, 2022
Acceptance Date
November 7, 2022
Published in Issue
Year 2023 Volume: 25 Number: 1
APA
Karakulak, E. (2023). Adaptive thresholding based low complexity QRS detection algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 78-89. https://doi.org/10.25092/baunfbed.1075661
AMA
1.Karakulak E. Adaptive thresholding based low complexity QRS detection algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023;25(1):78-89. doi:10.25092/baunfbed.1075661
Chicago
Karakulak, Ertuğrul. 2023. “Adaptive Thresholding Based Low Complexity QRS Detection Algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 25 (1): 78-89. https://doi.org/10.25092/baunfbed.1075661.
EndNote
Karakulak E (January 1, 2023) Adaptive thresholding based low complexity QRS detection algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 25 1 78–89.
IEEE
[1]E. Karakulak, “Adaptive thresholding based low complexity QRS detection algorithm”, Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 25, no. 1, pp. 78–89, Jan. 2023, doi: 10.25092/baunfbed.1075661.
ISNAD
Karakulak, Ertuğrul. “Adaptive Thresholding Based Low Complexity QRS Detection Algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 25/1 (January 1, 2023): 78-89. https://doi.org/10.25092/baunfbed.1075661.
JAMA
1.Karakulak E. Adaptive thresholding based low complexity QRS detection algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023;25:78–89.
MLA
Karakulak, Ertuğrul. “Adaptive Thresholding Based Low Complexity QRS Detection Algorithm”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 25, no. 1, Jan. 2023, pp. 78-89, doi:10.25092/baunfbed.1075661.
Vancouver
1.Ertuğrul Karakulak. Adaptive thresholding based low complexity QRS detection algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023 Jan. 1;25(1):78-89. doi:10.25092/baunfbed.1075661
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