Application of the R-peak detection algorithm for locating noise in ECG signals

2022 ◽  
Vol 72 ◽  
pp. 103316
Author(s):  
Božo Tomas ◽  
Mijo Grabovac ◽  
Karlo Tomas
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Ecg Signals ◽  
Peak Detection Algorithm

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

Fiber Bragg Grating Spectrum Peak-Detection Algorithm Based on Difference of Gaussian

2021 ◽  
Vol 58 (7) ◽  
pp. 0706002
Author(s):  
蔺彦章 Lin Yanzhang ◽  
刘毅 Liu Yi ◽  
潘玉恒 Pan Yuheng ◽  
李国燕 Li Guoyan
Keyword(s):  
Fiber Bragg Grating ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Bragg Grating ◽  
Peak Detection Algorithm ◽  
Difference Of Gaussian

Research of Peak-Detection Algorithm Based on Absolute Wavelength Calibration of Excimer Laser

2018 ◽  
Vol 45 (7) ◽  
pp. 0701003
Author(s):  
袁靖超 Yuan Jingchao ◽  
赵江山 Zhao Jiangshan ◽  
李慧 Li Hui ◽  
刘广义 Liu Guangyi
Keyword(s):  
Excimer Laser ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Wavelength Calibration ◽  
Peak Detection Algorithm

scholarly journals Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data

2019 ◽  
Vol 173 ◽  
pp. 35-41 ◽  
Author(s):  
Katrin Sippel ◽  
Julia Moser ◽  
Franziska Schleger ◽  
Hubert Preissl ◽  
Wolfgang Rosenstiel ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Peak Detection Algorithm

scholarly journals An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Qin ◽  
Jianqing Li ◽  
Yinggao Yue ◽  
Chengyu Liu
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Peak Detection Algorithm ◽  
Wavelet Multiresolution Analysis ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Mean Time

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

Combined Time-Frequency Calculation of pNN50 Metric From Noisy Heart Rate Measurements

2014 ◽  
Author(s):  
Payam Parsinejad ◽  
Yolanda Rodriguez-Vaqueiro ◽  
Jose Angel Martinez-Lorenzo ◽  
Rifat Sipahi
Keyword(s):  
Heart Rate ◽  
Mental Workload ◽  
Human Subjects ◽  
Detection Algorithm ◽  
Frequency Domain Analysis ◽  
Peak Detection ◽  
Time Frequency ◽  
Peak Detection Algorithm ◽  
Frequency Calculation ◽  
Noticeable Improvement

pNN50 is a metric derived from heart rate (HR) measurements, and it is known to correlate with mental-workload changes in human subjects. Conventionally, this metric is calculated based on the variability of successive time periods in peak-to-peak occurrences in HR data. In the case of noisy measurements of HR, however, peak-to-peak detection may not be reliable. Here, we present a combined time-frequency domain analysis, benefiting from Short Time Fourier Transform, by which we can more accurately extract pNN50 metric from noisy HR data. An experimental measurement with added noise is used as a benchmark problem to demonstrate the effectiveness of the approach with noticeable improvement over the conventional time domain peak-to-peak detection algorithm.

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