Denoising and Automated R:peak Detection in the ECG Using Discrete Wavelet Transform

Goodfellow, Jonathan; Escalona, OJ; Kodoth, Vivek; Manoharan, Ganesh; Bosnjak, Antonio

doi:10.22489/cinc.2016.301-506

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…where True Positives (TP) represents the peaks that have been correctly identified, False Positive (FP) is equal to a point that has been incorrectly identified as a peak, and False Positive (FN) represents any peak that has not been identified [10].…”

Section: Performance Assessmentmentioning

confidence: 99%

Wearable Technology: Signal Recovery of Electrocardiogram from Short Spaced Leads in the Far-field Using Discrete Wavelet Transform Based Techniques

McCallan¹,

Finlay²,

Biglarbeigi³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Bipolar ECG leads recorded from closely spaced electrodes are challenging in any context. When they are positioned distally with respect to the source field (far-field), the recovery of clinically useful signal content represents an even greater challenge. Due to the increased interest in ambulatory wellness devices, particularly wrist-worn devices, there is a renewed interest in recovering ECG signals from distally located bipolar leads. In this study 10 bipolar leads were simultaneously recorded at various locations along the left arm. At the same time, a conventional proximal reading on the chest using Lead I was also recorded and stored. This process was repeated for 11 healthy subjects. ECGs were recorded for a period of approximately 6 minutes for each subject and sampled at a frequency of 2048 Hz. Wavelet-based filtering using Daubechies 4 wavelet decomposition and soft threshold was applied to each lead. QRS detection performance was assessed against Lead I for each subject. This investigation found that a lead positioned transversally (using BIS gelled electrodes) on the upper arm provided the best accuracy against the benchmark QRS detection (SEN = 0.998, PPV = 0.984). The most distally positioned bipolar lead using dry electrodes faired least favourable (SEN = 0.272, PPV = 0.202).

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Section: Performance Assessmentmentioning

confidence: 99%

Wearable Technology: Signal Recovery of Electrocardiogram from Short Spaced Leads in the Far-field Using Discrete Wavelet Transform Based Techniques

McCallan¹,

Finlay²,

Biglarbeigi³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Adaptive filtering and template matching have been used to further improve the detection performance. Continuous wavelet transform with selective scale [27] and Shannon's energy [28] have also been used to detect R-peak locations, while discrete wavelet transform was used in [29]. In [30], the authors used sorting and thresholding squared double difference signals from ECG data to estimate R-peak locations.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect

Chowdhury

Khan

et al. 2023

Bioengineering

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Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient’s electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

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State-of-the-Art Method to Detect R-Peak on Electrocardiogram Signal: A Review

Maghfiroh

Musvika

Wakidi

et al. 2021

Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics

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Denoising and Automated R:peak Detection in the ECG Using Discrete Wavelet Transform

Abstract: This work presents a novel approach to ECG R-peak detection based on the

Cited by 6 publications

References 12 publications

Wearable Technology: Signal Recovery of Electrocardiogram from Short Spaced Leads in the Far-field Using Discrete Wavelet Transform Based Techniques

Wearable Technology: Signal Recovery of Electrocardiogram from Short Spaced Leads in the Far-field Using Discrete Wavelet Transform Based Techniques

Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect

State-of-the-Art Method to Detect R-Peak on Electrocardiogram Signal: A Review

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