scholarly journals Automatic QRS complex detection algorithm designed for a novel wearable, wireless electrocardiogram recording device

2012 ◽  
Author(s):  
Dorthe B. Nielsen ◽  
K. Egstrup ◽  
J. Branebjerg ◽  
G. B. Andersen ◽  
H. B. D. Sorensen
Keyword(s):  
Detection Algorithm ◽  
Recording Device ◽  
Qrs Complex ◽  
Qrs Complex Detection ◽  
Complex Detection

EFFICIENT QRS COMPLEX DETECTION ALGORITHM IMPLEMENTATION ON SOC-BASED EMBEDDED SYSTEM

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Muhammad Amin Hashim ◽  
Yuan Wen Hau ◽  
Rabia Baktheri
Keyword(s):  
Embedded System ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Moving Windows ◽  
Field Programmable ◽  
Complex Detection ◽  
On Chip

This paper studies two different Electrocardiography (ECG) preprocessing algorithms, namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection. Both algorithms are compared in terms of QRS detection accuracy and computation timing performance, with implementation on System-on-Chip (SoC) based embedded system that prototype on Altera DE2-115 Field Programmable Gate Array (FPGA) platform as embedded software. Both algorithms are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT-BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56.33 seconds computation while DB algorithm achieve 96.74% with only 22.14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result shows that the proposed optimized Moving Windows Integrator algorithm achieves 9.5 times speed up than original MWI while retaining its QRS detection accuracy. 

scholarly journals A Novel Wavelet-Based Algorithm for Detection of QRS Complex

2019 ◽  
Vol 9 (10) ◽  
pp. 2142 ◽  
Author(s):  
Chun-Cheng Lin ◽  
Hung-Yu Chang ◽  
Yan-Hua Huang ◽  
Cheng-Yu Yeh
Keyword(s):  
Evaluation Method ◽  
Decision Rules ◽  
Detection Algorithm ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Frequency Components ◽  
Noise Evaluation ◽  
Complex Detection ◽  
Almost All

Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

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