Research Article

Adaptive thresholding based low complexity QRS detection algorithm

Volume: 25 Number: 1 January 16, 2023
EN TR

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

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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