Detection R Peak in Electrocardiogram Signal Using Daubechies Wavelet Transform and Shannon’s Energy Envelope

Thirrunavukkarasu, R.R.; Meeradevi, T.; Ravi, Abirami; Ganesan, Dhivya

doi:10.1109/icaccs.2019.8728556

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…The most frequent physiological method used for measuring physical stress is heart rate variability (HRV), which is extracted from an electrocardiogram (ECG) [39][40][41]. An ECG represents the graphical record of an individual's heart's electrical activity, produced by the depolarization and repolarization of the atria and ventricles [42]. HRV is the variation in the time intervals between consecutive heartbeats [43] and can be used as a sensitive diagnostic technique for monitoring the activity of the autonomic nervous system (ANS) in stressful situations [16,44].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Alessa,

Alhaag,

Al-harkan

et al. 2023

Applied Sciences

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Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads.

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Section: Introductionmentioning

confidence: 99%

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Alessa,

Alhaag,

Al-harkan

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The ECG is described by the waveforms of the P, QRS, and T waves, which are associated with each heart-rate function. The P wave displays the process of depolarization of the atrial myocardium; the QRS complex displays depolarization of the ventricles; the ST segment, and the T wave displays the processes of repolarization of the ventricular myocardium [6]. Figure 1 shows an example of an electrocardiogram waveform with P, Q, R, S, and T characteristics, as well as standard electrocardiogram intervals are PQ intervals, ST intervals, and QRS complex.…”

Section: Introductionmentioning

confidence: 99%

System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing

2021

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Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals.

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“…Most of the state-of-the-art methods for R-peak detection are based on wavelet transform [8][9][10], simple mathematical operations [6,11,12], hidden Markov models, and machine learning. Wavelet transform is a suitable approach for considering the non-stationary behavior of the ECG signal.…”

Section: Introductionmentioning

confidence: 99%

A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection

Fotoohinasab,

Hocking,

Afghah

2021

Preprint

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The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal; thereby, the R-peak detection task can be relaxed from any preprocessing step. In this novel approach, prior biological knowledge about the expected sequence of changes is incorporated into the model using the constraint graph, which can be defined manually or automatically. First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme. Finally, we compare the manually defined graphs and learned graphs in terms of graph structure and detection accuracy. We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.

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Detection R Peak in Electrocardiogram Signal Using Daubechies Wavelet Transform and Shannon’s Energy Envelope

Cited by 10 publications

References 14 publications

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing

A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection

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