A robust-digital QRS-detection algorithm for arrhythmia monitoring

Ligtenberg, A.; Kunt, M.

doi:10.1016/0010-4809(83)90027-7

Cited by 84 publications

(26 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The five-point derivative prevents high-frequency noise amplification, in the present implementation high-frequency noise is further attenuated by the Kaiser Window filter [6]. Peaks are found by comparing the time-averaged signal to a primary threshold, derived from the threshold coefficient and the amplitude of previous peaks.…”

Section: Algorithms and Methodsmentioning

confidence: 99%

Acquisition and Analysis System of the Ballistocardiogram Signal Based on Virtual Instruments

Jiang

Yang

et al. 2011

AEF

View full text Add to dashboard Cite

Abstract. Ballistocardiogram signal (BCG) is a non-invasive technique for the assessment of the cardiac function. It consists mainly of heart movement and the movement of blood in aorta, arteries, and periphery, which can be used to real-time monitor the heart rate and respiration frequency at home. In our laboratory, a sitting BCG detection chair has been designed successfully, and the acquisition and analysis system based on virtual instruments is proposed in this paper. MATLAB7.0 and LabVIEW8.5 were used to simulate the operational environment, and the results show high efficiency and accuracy in displaying waveform and spectrum, extracting main characteristics of heart rate and respiratory frequency, and alerting when abnormal heart-rate occurs.

show abstract

Section: Algorithms and Methodsmentioning

confidence: 99%

Acquisition and Analysis System of the Ballistocardiogram Signal Based on Virtual Instruments

Jiang

Yang

et al. 2011

AEF

View full text Add to dashboard Cite

show abstract

“…The independent leads (I, II, V1, V2, V3, V4, V5, V6) are sampled simultaneously at a rate of 500 Hz; leads III, aVR, aVL and aVF are computed according to classic expressions based upon leads I and II [11]. Also, a digital moving-average filter proposed by Ligtenberg and Murat [10] is applied in real time. The filter general expression is the following:…”

Section: Methodsmentioning

confidence: 99%

QT dispersion monitoring as a coronary disturbance prediction tool

Gonzalez¹,

Rodríguez²,

Almeida³

2005

Computers in Cardiology, 2005

View full text Add to dashboard Cite

show abstract

“…In order to reduce noise, the filtering stage is constructed using bandpass filter (10-45 Hz) which accentuates the QRS complex and reduces noise since the frequency domain of available ECG signal is about 10-25 Hz [42]. Then, the filtered signal, f[n] (n denotes sampling points), is differentiated using five-point first-order differentiation to provide information about the slope of the QRS complexes and reduce the influence of the P and T waves [43]. The equation for five-point first-order derivative of the filtered ECG is listed as follows:…”

Section: Suppression Of Noise and Enhancement Of Qrs Complexmentioning

confidence: 99%

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Rui

Wang

et al. 2017

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

show abstract

A robust-digital QRS-detection algorithm for arrhythmia monitoring

Cited by 84 publications

References 6 publications

Acquisition and Analysis System of the Ballistocardiogram Signal Based on Virtual Instruments

Acquisition and Analysis System of the Ballistocardiogram Signal Based on Virtual Instruments

QT dispersion monitoring as a coronary disturbance prediction tool

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Contact Info

Product

Resources

About