A Novel Approach to ECG R-Peak Detection

Journal of Advanced Zoology, 2023

An electrocardiogram (ECG) is a continuous electrical signal from the heart that is recorded to understand the activity and condition of the heart. A recorded ECG signal always follows a defined pattern for a normal heart condition. Variation in the normal ECG pattern can be seen in cases of numerous cardiac abnormalities. A recorded ECG is also affected by a number of noises and distortions, resulting in a low SNR. A variation in ECG pattern can lead to incorrect study and improper diagnosis of heart condition. Thus, to perform an efficient analysis, it is necessary to preprocess the ECG waveform. ECG preprocessing requires noise removal and analysis of necessary features needed to study cardiac activity. In this paper, ECG preprocessing is evaluated by using two noise removal techniques, i.e., finite and infinite impulse response. After this, the R-peaks are detected using discrete wavelet transform (DWT), maximal optimal DWT, principal component analysis and independent component analysis. A wavelet transform technique is further proposed using Savitzky-Golay filtering and DWT. The results obtained from the proposed methodology represent the best results compared to those of other methods explicated in this paper.

This paper describes an algorithm for the automatic detection of R-waves in the electrocardiograph (ECG). The proposed algorithm deals with the development of energy based technique for the detection of R-wave in ECG that is of great importance in evaluating heart rate computation and serves as the basis for the extraction of other ECG features. The algorithm is based on the mainly window based energy analysis of the ECG signal. This energy domain offers an easy interpretation of ECG signal for the detection of R-waves in it. The areas under the ECG waveform where R waves are prominent appear as higher energy zones. Thereafter, thresholding and refractory period concepts are used to eliminate unrelated peaks appearing as R-waves. The proposed algorithm is equally applicable to ECG signals from modified lead-II as well as other leads. The algorithm is easier to implement just after removal of the low frequency samples in ECG signal. ECG signals are collected from MIT-BIH database and the algorithm is developed on the first five minute segment of each record.

2016

The research article proposes the effective method for R-peak detection in the ECG signal. The improper beating of the heart called cardiac arrhythmia which is risk to human. The ECG samples are taken from physionet (physio bank ATM). Analysis of ECG signal and detection of R-Peaks is discussed in this paper. Initially the noise is removed from the signal using FFT technique, windowing technique and thresholding technique to detect R-peaks. In the ECG signal processing one can encounter the difficulties like unequal distance between peaks, irregular peak form, occurrence of lowfrequency components due to patient breathing etc., In order to resolve and reduce the effect of these factors processing pipeline should contain particular stages which is discussed in the paper and the R-peak detection algorithm is implemented in MATLAB R 2012b.

Journal of Biomedical Engineering and Medical Imaging, 2016

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.

R-Peak detection in ECG signals have been a crucial topic in biomedical. The current R-peak detectors cope up with the unstable QRS morphology and noise during detection. Also, the detection rate is of equal importance. To overcome all these issues a new technique has been proposed in this paper for the R-peak detection at a very good detection rate. The proposed detector was tested on MIT-BITH database to prove its worth. The experimental analysis over MATLAB shows the superiority of the proposed detector over the current detectors.

— The Modern era is marked by tension and, therefore, heart is in trouble. Whole of the world is busy making research in ECG techniques. on Electrocardiogram and its feature extraction is the area of interest.. Analysis and classification let the medical professional to detect the heart ailments and other diseases. In our research method for De-noising of ECG signal and Feature extraction Algorithm is proposed. We considered the baseline wander problem in ECG signal which is basically due to the measurement error. This work involves the IIR filter Savitzky-Golay filter and Wavelet Transform. ECG signal is de-noised without affecting any information from ECG. We have also designed a feature Extraction algorithm which automatically detects the RR interval and QRS interval along with the amplitude of Q, R, and S. The whole algorithm including the de-noising of signal and Feature extraction. This work has been simulated on the MATLab software.

World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2012

The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In addition, this paper will include two main R peak detection methods by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on Dyadic Wavelet Transform DyWT. Keywords—Derived calculation methods, Electrocardiogram, R peaks, Wavelet Transform.

Biomedical papers of the Medical Faculty of the University Palacký, Olomouc, Czechoslovakia, 2007

The presence of parasite interference signals could cause serious problems in the registration of ECG signals and many works have been done to suppress electromyogram (EMG) artifacts noises and disturbances from electrocardiogram (ECG). Recently, new developed techniques based on global and local transforms have become popular such as wavelet shrinkage approaches (1995) and time-frequency dependent threshold (1998). Moreover, other techniques such as artificial neural networks (2003), energy thresholding and Gaussian kernels (2006) are used to improve previous works. This review summarizes windowed techniques of the concerned issue. We conducted a mathematical method based on two sets of information, which are dominant scale of QRS complexes and their domain. The task is proposed by using a varying-length window that is moving over the whole signals. Both the high frequency (noise) and low frequency (base-line wandering) removal tasks are evaluated for manually corrupted ECG signals...

2016 Computing in Cardiology Conference (CinC), 2016

This work presents a novel approach to ECG R-peak detection based on the Discrete Wavelet Transform.

2015

One of the major problems in the analysis of Electro-Cardiogram (ECG) signal compression is the accurate detection of R-peak. It is due to the difficulties varied by the time varying morphology of ECG, the physiological variations due to the patient and the noise contamination. However, it includes power line interface, muscle contraction noise, poor electrode contact, patient movement, and baseline wandering due to respiration. R-peak applications require accurate heart beat monitoring systems including intensive care units, operating rooms, implantable pacemakers and defibrillators. Moreover, it detects a QRS complex when ECG amplitude exceeds a threshold level. If the threshold is too high, true beats can be missed. If the threshold is too low, false detection can result during EMG artifact and external interface. So, during these artifacts, the magnitude of the noise can become larger than the signal, QRS detection based on amplitude threshold alone is not satisfactory. The prob...