A Robust QRS Complex Detection Algorithm Using Dynamic Thresholds

Elgendi, Mohamed; Mahalingam, S.; Jonkman, Mirjam; Boer, Friso De

doi:10.1109/csa.2008.16

Cited by 35 publications

(20 citation statements)

References 22 publications

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“…The heartbeats in ( ) k Y (see (12)) are the normal beats, premature ventricular contraction, and atrial premature contraction. The total number of heartbeats is 36 N = .…”

Section: Resultsmentioning

confidence: 99%

Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals

Rodriguez

Mexicano²,

Bíla³

et al. 2014

2014 International Symposium on Computer, Consumer and Control

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This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches.

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“…The heartbeats in ( ) k Y (see (12)) are the normal beats, premature ventricular contraction, and atrial premature contraction. The total number of heartbeats is 36 N = .…”

Section: Resultsmentioning

confidence: 99%

Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals

Rodriguez

Mexicano²,

Bíla³

et al. 2014

2014 International Symposium on Computer, Consumer and Control

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show abstract

“…In general, the ECG signal of a single cardiac cycle lies on the P, T, and QRS complex waves as depicted in Figure 1. Sometimes a U-wave may also be present after the T-wave (Elgendi et al, 2009).…”

Section: Detection Of the Qrs Complexmentioning

confidence: 98%

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

Rodriguez¹,

Mexicano²,

Bíla³

et al. 2015

Journal of Applied Research and Technology

107

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“…The peak detection difficulties occur when the input electrical signal is disrupted by unwanted noise and interference [9]. Mohamed Elgendi is improved QRS detection algorithm using dynamic thresholds [2]. Jiapu Pan developed an algorithm for QRS detection which was based on slope, amplitude, and width [3].…”

Section: Introductionmentioning

confidence: 99%

Noise Removal and QRS Detection of ECG Signal

Mou¹,

Islam²,

Huang³

et al. 2016

JBEMi

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In electrocardiogram (ECG), noise removal and QRS complex play the vital role for detecting various heart diseases. So, noise free and accurate QRS detection becomes very important in ECG signal. In this paper we are going to describe a new algorithms which are able to make it noise free and detect QRS complex in ECG signal. Generally, a noise free algorithm removes the noisy signal and we have used Remez exchange algorithm for 1st algorithm, designed an arbitrary magnitude with FIR filter for 2nd algorithm and FIR filter with window method for 3rd proposed algorithms. The noise free ECG signal of QRS complex can be detected by proposed detection algorithms. The performance parameters are SNR, PRD, MSE and Correlation and accuracy, sensitivity, specificity, precision are used to justify the proposed noise free algorithm and QRS detection. The real data examples and experimental results approve new algorithms which are more effective in ECG applications.

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A Robust QRS Complex Detection Algorithm Using Dynamic Thresholds

Cited by 35 publications

References 22 publications

Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals

Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

Noise Removal and QRS Detection of ECG Signal

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