scholarly journals A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4003 ◽  
Author(s):  
Aiyun Chen ◽  
Yidan Zhang ◽  
Mengxin Zhang ◽  
Wenhan Liu ◽  
Sheng Chang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Threshold Strategy ◽  
Pd Control ◽  
Ecg Signals ◽  
Field Programmable ◽  
Exponential Transform ◽  
Current Threshold ◽  
Basic Characteristics

As one of the important components of electrocardiogram (ECG) signals, QRS signal represents the basic characteristics of ECG signals. The detection of QRS waves is also an essential step for ECG signal analysis. In order to further meet the clinical needs for the accuracy and real-time detection of QRS waves, a simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection is proposed. The exponential transform (ET) and proportional-derivative (PD) control-based adaptive threshold are designed to detect QRS-complex. The proposed ET can effectively narrow the magnitude difference of QRS peaks, and the PD control-based method can adaptively adjust the current threshold for QRS detection according to thresholds of previous two windows and predefined minimal threshold. The ECG signals from MIT-BIH databases are used to evaluate the performance of the proposed algorithm. The overall sensitivity, positive predictivity, and accuracy for QRS detection are 99.90%, 99.92%, and 99.82%, respectively. It is also implemented on Altera Cyclone V 5CSEMA5F31C6 Field Programmable Gate Array (FPGA). The time consumed for a 30-min ECG record is approximately 1.3 s. It indicates that the proposed algorithm can be used for wearable heart rate monitoring and automatic ECG analysis.

A REAL-TIME QT INTERVAL DETECTION ALGORITHM

2008 ◽  
Vol 08 (02) ◽  
pp. 251-263 ◽  
Author(s):  
Z. E. HADJ SLIMANE ◽  
F. BEREKSI REGUIG
Keyword(s):  
Real Time ◽  
Qt Interval ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Signal To Noise ◽  
Ecg Signals ◽  
Wave Detection ◽  
T Wave ◽  
Electrocardiogram Ecg ◽  
Ventricular Depolarization

The QT interval is the electrocardiographic representation of the duration of ventricular depolarization and repolarization. In this paper, we have developed a new real-time QT interval detection algorithm for automatically locating the onset of QRS and the end of the T wave. The algorithm consists of several steps: signal-to-noise enhancement, QRS detection, QRS onset, and T-wave end definition. The detection algorithm is tested on electrocardiogram (ECG) signals from the universal MIT-BIH Arrhythmia Database. The resulting QRS detection algorithm has a sensitivity of 99.79% and a specificity of 99.72%. The QRS onset and T-wave detection algorithm is tested using several data records from the MIT/BIH Arrhythmia Database. The results obtained are shown to be highly satisfactory.

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 Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

2018 ◽  
Vol 175 ◽  
pp. 02008 ◽  
Author(s):  
Daizong Yang ◽  
Yue Zhang
Keyword(s):  
Real Time ◽  
Hilbert Transform ◽  
High Sensitivity ◽  
Detection Algorithm ◽  
Center Frequency ◽  
Qrs Detection ◽  
Threshold Method ◽  
Physiological Signal ◽  
First Derivative ◽  
Adjustable Center

Electrocardiogram(ECG) is an important physiological signal of the human body. It is widely used in identification and arrhythmia detection. The first step of ECG application is signal segmentation, that is, the QRS detection. An effective and real-time QRS detection algorithm is proposed in this paper. A differentiator with adjustable center frequency is used to capture the first derivative information of the frequency band of the electrocardiogram. Then Hilbert transform is used to generate the envelope of the first derivative. After that, a dual threshold method is introduced to decrease FP and FN. Finally, a more precise R wave position is determined based on derivative method. The detector is validated on MIT-BIH arrhythmia database. The result show that the proposed algorithm has a high Sensitivity of 99.87%, Specificity of 99.84%, and the detection error rate is 0.28%. The average execution time of a 30 minutes record is 2.45s.

scholarly journals Application Analysis of Wearable Technology and Equipment Based on Artificial Intelligence in Volleyball

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xianyan Dai ◽  
Shangbin Li
Keyword(s):  
Real Time ◽  
Sports Injuries ◽  
Recognition Performance ◽  
Wearable Technology ◽  
Detection Algorithm ◽  
The Body ◽  
Self Assessment ◽  
Ecg Signals ◽  
Exercise Fatigue ◽  
Fatigue Detection

Today, while people’s satisfaction with materials is high, the pursuit of health has begun and sports are becoming increasingly important. Volleyball is a good physical and mental exercise, which helps improve the health of the body. However, excessive exercise usually leads to muscle strain and more serious accidents. Therefore, how to effectively prevent excessive fatigue and sports injuries becomes more and more important. In the past, some methods of exercise fatigue detection were mostly self-assessment through some indicators, which lacked real-time and accuracy. With the advancement of smart technology, in order to better detect sports fatigue, smart wearable technology and equipment are used in volleyball. Firstly, surface electromyography signals (sEMG) are collected through wearable technology and equipment. Secondly, the signal is preprocessed to extract features that are conducive to exercise fatigue assessment. Finally, a motion fatigue detection algorithm is designed to identify and classify features and evaluate the motion status in real-time. The simulation results show that it is feasible to collect ECG signals and EMG signals to detect exercise fatigue. The algorithm has good recognition performance, can evaluate exercise conditions in real-time, and prevent fatigue and injury during exercise.

A Real-Time QRS Detection Algorithm

1985 ◽  
Vol BME-32 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Jiapu Pan ◽  
Willis J. Tompkins
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Qrs Detection

scholarly journals Real-time QRS detection using integrated variance for ECG gated cardiac MRI

2016 ◽  
Vol 2 (1) ◽  
pp. 255-258 ◽  
Author(s):  
Marcus Schmidt ◽  
Johannes W. Krug ◽  
Georg Rose
Keyword(s):  
Real Time ◽  
Cardiac Mri ◽  
Vital Signs ◽  
Peak Detection ◽  
Resonance Imaging ◽  
Qrs Detection ◽  
Ecg Signals ◽  
Integrated Variance ◽  
Magnetic Resonance Imaging Mri ◽  
Electrocardiogram Ecg

AbstractDuring magnetic resonance imaging (MRI), a patient’s vital signs are required for different purposes. In cardiac MRI (CMR), an electrocardiogram (ECG) of the patient is required for triggering the image acquisition process. However, a reliable QRS detection of an ECG signal acquired inside an MRI scanner is a challenging task due to the magnetohydrodynamic (MHD) effect which interferes with the ECG. The aim of this work was to develop a reliable QRS detector usable inside the MRI which also fulfills the standards for medical devices (IEC 60601-2-27). Therefore, a novel real-time QRS detector based on integrated variance measurements is presented. The algorithm was trained on ANSI/AAMI EC13 test waveforms and was then applied to two databases with 12-lead ECG signals recorded inside and outside an MRI scanner. Reliable results for both databases were achieved for the ECG signals recorded inside (DBMRI: sensitivity Se = 99.94%, positive predictive value +P = 99.84%) and outside (DBInCarT: Se = 99.29%, +P = 99.72%) the MRI. Due to the accurate R-peak detection in real-time this can be used for monitoring and triggering in MRI exams.

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